Skip to content
If it doesn't work, you don't pay. 30 days.
Implementa.

AI solutions

Concrete problems. Solutions that ship.

We don’t sell “AI.” We fix things that cost you hours today: answering messages, sorting documents, chasing leads. Each solution gets built and left running.

By area

Explore by work area

Email and communicationsFinance and accountingSales and CRMCustomer service

By problem

Automate expense reports: from the receipt in someone’s pocket to the reimbursement in payroll, without chasing anyone

An expense report is a five-minute chore that costs the company two weeks: the employee can’t find the receipt, the manager approves without looking, and finance chases paperwork right up to close. We automate the whole loop —capture, policy, approval and reimbursement— on your systems.

See solution

Answer WhatsApp around the clock, without anyone on call

Your business gets messages at 11pm and on Sundays. Right now they sit unread until someone picks up their phone. That gets automated — and you feel it the first week.

See solution

Orders come in through six different channels and someone types them in one by one

Email with the PDF attached, an Excel every customer laid out their own way, the distributor portal, an EDI only the two big accounts use and, still, a scanned fax. At the end of the funnel there’s a person copying part numbers into the ERP by hand. That’s a job, not a destiny: it gets automated.

See solution

Ship quotes in minutes, not the next day

They ask for a price on Tuesday and you send it Thursday. By then the customer has three quotes on the table, and the one that landed first has the edge. That gets automated — and it shows in your win rate.

See solution

Pull the data off your invoices without typing them in by hand, one by one

Every invoice that lands, someone opens it, reads the amount, the vendor, the date and the tax, and keys it into the ERP. Multiply that by what you get a month. That gets automated — and it shows in hours and in errors.

See solution

Cut your call volume without leaving anyone unanswered

The line rings all day and it’s almost always the same thing: where’s my order, what are your hours, how do I move my appointment. While your team repeats answers, the cases that actually matter wait in the queue. That gets automated — and you feel it the first week.

See solution

Respond to RFPs without burning a week per bid

Every tender is hundreds of pages of requirements plus the same answers you already wrote last month, buried in folders. While you dig, the deadline runs. That gets automated — and you bid on more deals with the same team.

See solution

Generate the sales proposal in an afternoon, not three days

The meeting went well, and now comes the usual grind: dig up the last client’s proposal, swap the logo, rewrite half the document, and cross your fingers. Three days later you send it — if the client’s still warm. That gets automated, and you feel it in what you close.

See solution

Classify tickets automatically and send each one to the right person

Every ticket that comes in goes through a person first: read it, figure out what it’s about, set a priority, push it to the right queue. When they get it wrong —and they do— the ticket bounces from agent to agent while the customer waits. Classifying tickets automatically kills that toll: the system tags, prioritizes, and routes on arrival, and your team opens only what’s theirs.

See solution

Classify the documents that land and file each one where it belongs, on their own

Everything lands in your shared inbox and your Drive: contracts, resumes, IDs, forms, delivery notes, emails with attachments. Someone opens them one by one, figures out what type they are, and files them in the right folder or system. That gets automated: the system recognizes each one by type, renames it, and drops it where it belongs on arrival.

See solution

Reply to every lead in minute one, not whenever someone has a gap

The form comes in at 12:41. Your rep sees it at 4:30pm, between meetings. By then the customer has filled out three other forms and is talking to whoever replied first. That gets automated — and it shows in your contact rate.

See solution

The same five emails, twenty times a day. That gets automated.

“Where’s my order?”, “can you resend the invoice?”, “what are your hours?”. Your team writes the same reply over and over, swapping two details. It’s not that they work badly: they’re doing by hand a job a system does on its own. We build it on your inbox and your tools, and leave it answering.

See solution

You close the deal and the contract takes three days to go out. That gets automated.

The client says yes, and then someone has to open the template, change the name, drop in the price, adjust the clauses and not mess it up. Days of delay between the “yes” and the signature. It gets automated: the system generates the ready-to-sign contract from your templates and the deal data.

See solution

One shared inbox where everything lands together and nobody knows what’s theirs. That gets sorted on its own.

info@, sales@, support@: it all comes in mixed together —an urgent order, an invoice, a pushy salesperson, spam— and someone has to open each email just to decide whose it is and forward it. That manual triage gets automated: the system reads what comes in, classifies it and puts it in the right hands, instantly.

See solution

You're signing contracts nobody has read all the way through. The bill for that shows up two years later.

The big customer's framework agreement, the supplier's terms, the insurance policy, the SaaS that renews itself. It lands on Thursday, it has to be signed Friday, and somebody skims it. We automate the reading: the system checks every contract you receive against your own written position, flags the clause you should not be accepting, and watches the renewal dates nobody is watching today.

See solution

By software

You run the whole company in Holded. And someone is still typing in what Holded already knows.

Holded centralizes invoicing, accounting, CRM and projects —but the data still goes in by hand, invoices get reconciled one by one, and follow-ups depend on someone remembering. On top of Holded’s API we build the AI that does that repetitive part on its own.

See solution

You pay for HubSpot so the team can sell. And the team spends the day feeding HubSpot.

HubSpot gives you a CRM, pipelines, sequences and reporting —but contacts come in duplicated, call notes get typed by hand, follow-ups depend on someone checking the pipeline, and Monday’s report is built by exporting and pasting. On top of HubSpot’s API we build the AI that does that part on its own.

See solution

You run the whole company on Odoo. And the team spends the day feeding Odoo by hand.

Odoo gives you CRM, inventory, sales, purchasing, invoicing and accounting in one place —but orders get keyed from one module to the next, the delivery note gets typed off the email, stock gets squared by exporting to Excel, and the same data hops between Odoo and a spreadsheet. On top of Odoo’s API we build the AI that does that mechanical part on its own.

See solution

Salesforce stores every contact, every opportunity and every note. What it doesn’t do is tell you who to call today.

Salesforce gives you a CRM, a pipeline, reports and even Einstein —but accounts come in half-filled, the score depends on fields nobody completes, and which deal is at risk gets decided by gut on Monday morning. On top of Salesforce’s API we build the AI that reads your CRM and turns it into the sales decision: who to call, why, and with what context.

See solution

You built the whole company in Notion. And now someone spends their day filling properties, summarizing pages and moving cards by hand.

Notion gave you databases, wikis, projects and docs in one place, flexible like nothing else —but keeping it current is human work: properties get filled by hand, nobody summarizes the pages, status gets updated when someone remembers, and pulling a decision out of it means reading half the workspace. On top of Notion’s API we build the AI that reads your workspace, keeps it current on its own, and turns it into the answer you were after.

See solution

You picked Zoho CRM because it costs little and does a lot. Then the data comes in half-filled and the sales decision gets made by gut.

Zoho CRM gives you modules, workflows, Deluge functions and even Zia at a price no pricey CRM matches —but leads come in unfilled, the score comes from fields nobody completes, and which deal is at risk gets decided by the rep on Monday morning. On top of Zoho CRM’s API we build the AI that reads your CRM and turns it into the sales decision: who to call, why, and with what context.

See solution

Pipedrive shows you the pipeline. What it doesn’t do is move it for you.

Pipedrive paints the stages, the deals and the tasks —but it only moves when the rep updates it by hand, follow-up falls through the cracks the moment things get busy, and whether a deal is dead or alive gets decided by gut—. On top of Pipedrive’s API we build the AI that logs activity on its own, fires the next step and keeps the pipeline current without anyone typing.

See solution

Your store sells on its own in Shopify. And behind it, someone answering "where’s my order?" all day.

Shopify charges, invoices and manages your catalog —but the repetitive work around every sale is still manual: answering order status, flagging stockouts, recovering carts, writing the new product page. On top of Shopify’s API we build the AI that eats that part.

See solution

You run the whole company inside Microsoft 365. And the team spends the day moving data from Outlook to Excel to SharePoint by hand.

Microsoft 365 gives you email, sheets, documents, SharePoint and Teams in one place —but the order lands in Outlook and someone keys it into an Excel, the report gets built by pasting data from three files, the document gets filed by hand in the right folder, and the same data hops between mail, sheet and SharePoint. On top of the Microsoft Graph API we build the AI that does that mechanical part on its own.

See solution

The whole company runs on Google Workspace. And the team spends the day moving data from Gmail to a Sheet to Drive by hand.

Gmail, Sheets, Docs, Drive and Chat give you everything in one place —but the order lands in Gmail and someone keys it into a Sheet, the report gets built by pasting data from three sheets, the attachment gets renamed and dragged into the right Drive folder, and the same data hops between mail, sheet and Drive. On top of the Google Workspace APIs we build the AI that does that mechanical part on its own.

See solution

Business Central already ships with Copilot and with agents. And your team is still typing, because the work doesn’t happen inside Business Central: it happens between Business Central and everything else.

Microsoft has done its part: Copilot is included in your license and the Payables and Sales Order agents work end to end inside the ERP. What they don’t cover is what comes in through the odd email, the sales rep’s Excel, the PDF that isn’t a PDF and the industry system that doesn’t talk to Business Central —nor what those agents are going to cost you every month, which is billed by consumption. We build the layer that does that work, and we put a number and a brake on the one you already have running.

See solution

Your books and payroll live in Sage. And someone spends the day keying invoices into Sage by hand.

Sage 50, Sage 200, Sage Despachos, Sage accounting and invoicing run your books, your ERP and your payroll —but the supplier invoice lands as a PDF and someone types it in entry by entry, the bank gets reconciled line by line, and data hops from email and Excel into Sage by hand. On top of Sage’s APIs and connectors we build the AI that does that mechanical part on its own.

See solution

You run the store on PrestaShop, on your own server. And someone spends the day adding products, squaring stock and answering supplier emails by hand.

PrestaShop is yours: open source, on your hosting, with your database and your modules. It runs your catalog, your orders and your customers —but adding products, updating stock, the order and supplier emails and the data hopping between PrestaShop, the ERP, Excel and email are still done by hand. On top of PrestaShop’s webservice API and your own install we build the AI that eats that mechanical part.

See solution

By agent

Hiring an SDR costs months of training and they leave within a year. The agent that prospects, qualifies and books doesn’t leave —we leave it running in your funnel.

A good SDR builds the list, writes the first message, chases the reply, qualifies it against your criteria and books the meeting with the closer —and then burns out, moves on, or takes three months to ramp. We ship that SDR as an agent in your funnel: it prospects, personalizes outreach, qualifies every reply against your ICP and books only the meetings worth having, with a person approving before it acts. We don’t sell you a sequencing tool; we leave the digital employee running on your CRM.

See solution

Requesting quotes, comparing them and placing the purchase order eats Purchasing’s whole week. The agent that does that work doesn’t take vacation days —we leave it running on your ERP.

A good buyer turns the internal request into a formal purchase request, asks the right suppliers for a quote, compares price and lead time, places the purchase order and tracks it through to delivery. The problem is that work is repetitive, done by hand in spreadsheets and email, and every hour spent there is an hour not spent negotiating a better price. We ship that work as an agent on your ERP: it turns the request into a purchase order request, asks for and compares quotes, generates the order and tracks it through to receiving, with a person approving the spend before it goes out. We don’t sell you a request form; we leave the purchasing work done.

See solution

Checking quality by hand only reaches a sample: the rest goes out unseen. The agent that reviews 100%, catches what's off and opens the corrective action —we leave it running on your systems.

Good quality control reviews the work against a criterion, flags what doesn't meet it, classifies it and opens the corrective action so it won't happen again. The problem is that by hand it doesn't scale: you review a sample, the rest ships unseen, and the defect surfaces once the customer already has it. We ship that control as an agent on your systems: it reviews every case against your checklist, catches the non-conformities, classifies them and opens the corrective action, with a person validating the doubtful ones. We don't sell you a checklist in a PDF; we leave the quality control done.

See solution

Half the calls that come in after hours are lost. The receptionist that answers, handles the question and books the appointment never hangs up on anyone —we leave it working on your number.

A good receptionist picks up on the first ring, greets callers in your tone, answers the usual —hours, price, where you are, whether there's an opening—, books the appointment on your calendar and passes only what truly needs a person to one. The problem is that seat doesn't cover 11pm, or Saturday, or five calls at once. We ship that receptionist as an agent on your number and your chat: it handles every call and message, answers the same old questions, books the appointment against your real calendar and routes the urgent stuff to you —24/7 and without hiring anyone. We don't sell you a phone system; we leave the digital employee working.

See solution

Your admin team spends the month keying in invoices, reconciling the bank and chasing payments. The AI accounting agent does that mechanical work and leaves the person to just validate: we leave it running on your ERP.

A good bookkeeper captures the invoice, extracts its data, assigns it to the right account and cost center, posts it to the ERP, reconciles the bank movement and chases what’s overdue. The problem is almost all of that is typing, matching and chasing —hours of mechanical work that eat the team alive and blow up the close. We ship that bookkeeper as an agent on your system: it reads every incoming invoice, dumps the validated data, proposes the entry, reconciles against the statement and leaves you only the exception to approve. We don’t sell you another invoicing tool; we leave the digital employee doing the books, with a person supervising what isn’t obvious.

See solution

Your admin team spends the day copying data from one system to another, filling in forms and updating records in the ERP and the CRM, and chasing the info that’s missing. The AI admin agent does that mechanical back-office work and leaves the person to just validate: we leave it running on your systems.

A good admin person picks up what comes in, drops it where it belongs, keeps the records up to date, files every document in its folder, chases what’s missing to close the case and puts together the weekly internal report. The problem is almost all of that is keying the same data several times, filling templates and chasing people —hours of mechanical work that eat the team alive and leave the records out of date. We ship that person as an agent on your systems: it reads what comes in, dumps the validated data into the ERP or the CRM, fills the form, files the document, chases what’s missing and prepares the recurring report, and leaves you only the exception to approve. We don’t sell you another management tool; we leave the digital employee doing the paperwork, with a person supervising what isn’t obvious.

See solution

Your sales rep spends half the week updating the CRM, chasing follow-ups and building quotes instead of selling. The AI sales agent does that back-office work and leaves the person to just close: we leave it running on your pipeline.

A good sales rep keeps the CRM alive after every call, chases the follow-up at the right moment, builds the quote off the catalog, revives the deal that went quiet and shows up to the meeting with the context ready. The problem is almost all of that is back office, not selling —keying, chasing, prepping—, and those are hours that eat the rep alive while deals cool off. We ship that support as an agent on your pipeline: it updates the opportunity after every contact, schedules and chases the follow-ups, builds the quote off your catalog, flags the stalled deal and preps the rep before the call —and leaves the person to just close and own the relationship. We don’t sell you another CRM tool; we leave the digital employee doing the back office of the sale, with a person closing.

See solution

Every ticket that comes in, someone opens it, hunts the answer in the knowledge base, writes the reply and, if it applies, jumps into another system to do the action —the refund, the exchange, the order status. The AI support agent does that whole case end to end and leaves a person only the exception: we ship it on your helpdesk and your systems and leave it running.

A good support agent reads the ticket, gets what it’s about, looks up the answer in the docs, replies and —when needed— goes into the system to run the action: processes the refund, handles the exchange, checks the order status. The problem is most of those cases are repeatable and resolvable, and they still eat the team alive one case after another. We ship that agent as a digital employee on your helpdesk and the systems where the action runs: it reads the incoming ticket, finds the answer in your knowledge base, writes and sends the reply, does the action end to end and escalates only the exception to a person. It’s not the 24/7 support channel or the one that routes the tickets: it’s the one that resolves them. We build it on what you already use and leave it running, with a person supervising what isn’t obvious.

See solution

You’ve got overdue invoices and nobody with time to chase them one by one. The AI collections agent chases what’s outstanding at the right moment, reconciles the payment when it lands and escalates the tough case —and a person approves what goes out to the customer: we leave it running on your billing.

Chasing what’s overdue is a job of persistence, not talent: checking every day who’s past due, sending the reminder in the right tone, turning up the pressure when it’s time, logging the promise to pay, matching the money to its invoice when it finally lands, and knowing when the case stops being an email and needs a person. The problem is almost nobody does it well, because it’s tedious and nobody enjoys fighting for the house’s money —so it gets chased late, in fits and starts, and cash suffers. We ship that collector as an agent on your billing: it watches the overdue book, drafts and schedules the dunning sequence, logs the promises, reconciles the payment to its invoice and escalates the tricky case to you. The rule is firm: the agent proposes what goes out and a person approves it; it never collects or moves money on its own. We don’t sell you another billing tool; we leave the digital employee chasing what’s outstanding, with a person in charge of what goes out.

See solution

Leadership asks for the cash-flow forecast and budget-vs-actuals, and someone spends two days exporting the ERP to Excel to build them —late and with last week’s data. The AI finance agent reads the numbers already in your books, updates the cash-flow forecast, crosses budget against actuals, catches the variances and builds the board report: it doesn’t keep the books, it reads them to decide. We leave it running on your ERP.

A good finance director looks at the numbers already sitting in the books and turns them into decisions: how much cash there’ll be eight weeks out, which lines are running over budget, why this month’s margin doesn’t match the plan, and what to tell the board on Monday. The problem is that before you decide there’s a mountain of mechanical work —exporting the ERP, pasting into Excel, reconciling versions and refreshing the same dashboard every week— that eats the finance team alive and makes the forecast land late and with stale data. We ship that work as an agent on your books: it reads the balances, movements and due dates you already have, keeps the cash-flow forecast alive, crosses budget against actuals, flags the variance the moment it shows and builds the board report —and leaves the decision to you. It doesn’t capture invoices or chase payments; the numbers are already there, this agent reads them. We don’t sell you another Power BI; we ship the financial analysis done, with a person validating before it reaches the board.

See solution

A good recruiter spends the day booking interviews, prepping briefs and chasing candidates. The agent does that work; your team decides who to hire.

Hiring well is human judgment: who fits, who doesn’t, who you bet on. But around that decision sits a pile of mechanical work that eats the HR calendar —lining up interview slots, prepping each candidate’s documentation, collecting interviewers’ feedback and telling waiting candidates where they stand. We ship an agent that does exactly that: it coordinates the hiring process end to end on your tools, and leaves people the one part you can’t delegate —deciding who you hire. We don’t sell you another ATS; we leave the coordination work done.

See solution

Searching, reading and summarizing to prepare a decision eats a whole afternoon. The agent that does that research never gets tired of reading: it hands you the brief with the sources.

Good research means reading ten sources, keeping what matters, reconciling what contradicts, and leaving it in a summary someone can actually decide with. The problem is that work is done by hand, tab after tab and copy-paste, and every brief eats half a day you don't spend deciding with it. We deploy that work as an agent: it gathers information from the sources you define, synthesizes it into a brief with every data point cited to its origin, and delivers it in the format you already use. We don't sell you a search box; we hand you the research done, with the sources on display so you can check it.

See solution

By industry

AI for training academies: every prospect's question answered instantly and enrollment handled on its own

The prospect asks about the course on a Sunday night and you open it Monday: they've already signed up with the academy that replied in five minutes. That gets automated — first reply, course info, enrollment and reminders — and you feel it in this week's sign-ups, not the quarter.

See solution

AI for restaurants: every booking and every message answered instantly, without dropping the tray

The reservation call comes in mid-rush, nobody picks up, and that table goes to another restaurant. Answering bookings and messages on the spot — by phone, WhatsApp, Instagram or Google — gets automated, and you feel it in covers, not a year from now.

See solution

AI for insurance brokers: every request answered on the spot and every renewal chased on its own

The client asks for a quote on a Sunday, you reply on Tuesday, and by then they've already signed with someone else. Answering the request instantly, chasing the renewal before it lapses and spotting the client who's missing a policy gets automated — and you feel it in the book, not a year from now.

See solution

AI for clinics: a full schedule and an answered phone, without expanding the front desk

Calls get missed at midday, open slots go unfilled, and patients don't warn you they're not coming. That gets automated — reception, reminders and answers — and you feel it in occupancy the first week.

See solution

AI for real estate: every portal lead answered within the minute and the viewing booked on its own

A lead from Idealista or Zillow that takes an hour to hear back already belongs to another agency. AI replies instantly, qualifies the prospect, offers a viewing and drops it in the calendar — day, night and across twenty listings at once.

See solution

AI for law firms: the mechanical part of the case —templates, clause review, deadlines, missing documents— done on its own, and your lawyers on what gets signed

A law firm doesn't get paid to fill in templates or chase the missing power of attorney. It gets paid for legal judgment. But the day goes on the mechanical stuff: the same brief adapted case by case, reviewing the contract clause by clause, watching deadlines, chasing the client for documents. AI does that repetitive part over your case files and leaves your lawyers to review, decide and sign.

See solution

AI for accounting firms: the practice's repetitive work — invoices, reconciliations, returns, registrations — done on its own, and your people on what actually bills

An accounting firm doesn't live on keying invoices or typing the same return company by company. But the month goes on exactly that. AI does the mechanical part of each client — invoice entry, reconciliation, return prep, the same old replies — and leaves your team to review and advise, which is what you're paid for.

See solution

AI for hotels: every guest message answered instantly, in their language and at any hour

The parking question lands at 11:40pm on Booking, in German, and the front desk sees it at 8am — by then the guest booked elsewhere or arrives annoyed. Answering on the spot — in their language, on their channel — gets automated, and you feel it in reviews and direct bookings, not a year from now.

See solution

AI for car dealerships: every portal lead answered in a minute and the test drive booked on its own

The Cars.com lead comes in Saturday at 9pm and you open it Monday — the dealer who replied in five minutes already has the appointment. That gets automated — first response, qualification and the test-drive booking — and you feel it in walk-ins the first week, not the quarter.

See solution

AI for logistics: every "where's my order?" answered on its own and the delivery incident caught before the customer

The phone and inbox fill up with "where's my shipment?" while the delay is already happening and nobody has seen it. That gets automated — order status instantly, proactive delivery tracking and the incident caught before the customer feels it — on your TMS/ERP, and you feel it in this week's calls, not the quarterly KPI.

See solution

AI for travel agencies: every query answered on the spot and the quote ready while the traveler is still deciding

A trip request lands Sunday night on WhatsApp, and until Monday nobody compares suppliers or builds the itinerary — by then the traveler booked on their own or with another agency. Answering instantly and having the first quote in hours, not two days, gets automated, and you feel it in the sales you close, not next year.

See solution

AI for construction firms: the tender squared away and the site paperwork up to date, without burning out your site manager

A tender is hundreds of pages someone has to read in full so no line item slips through, and the site generates a constant drip of RFIs, minutes and certificates done by hand between calls. AI doesn't lay the brick, but it reads the tender, requests prices from subcontractors and keeps the paperwork current so your people decide instead of type.

See solution

By integration

Notion and Slack are already connected. What isn't connected is what gets decided in Slack and what's on record in Notion.

The official integration shows previews and sends alerts. It doesn't create the task, doesn't update the database property, doesn't understand a thread and can't tell a decision from a conversation. We build that layer on top of your Slack and your Notion, and leave it running.

See solution

Teams and SharePoint are already integrated. What isn't integrated is the judgment.

Every Team gets its SharePoint site and every channel its folder — that part ships out of the box. What doesn't ship is the document arriving knowing what it is, which client it belongs to, which version it is and who approved it. We build that layer — classification, metadata and decisions — on your tenant and leave it running.

See solution

Zapier gets the email into Notion. And leaves it there, as a block of text nobody reads.

Moving the email is the easy part. The hard part is having it arrive knowing which project it belongs to, what got promised, by when and who decides — written into your database properties, not pasted inside a page. We build that bridge between your Gmail and your Notion and ship it to production.

See solution

Connecting HubSpot to Gmail takes two clicks. Getting the CRM to tell the truth doesn't.

The extension logs emails. What it doesn't do is decide which ones matter, which deal they belong to, what got promised inside and what happens next. We build that layer between your Gmail and your HubSpot.

See solution

Your rep closes the deal on WhatsApp. HubSpot only learns whatever he remembers to type in.

Integrating WhatsApp with HubSpot is not putting the chat inside the CRM: the native channel already does that. What nobody does is turn the conversation into a contact, a deal, a stage and a next step without someone copying it over. We build that AI layer on your portal and your number, and we ship it to production.

See solution

Every WooCommerce order should come out of Holded as an invoice. Today it comes out of someone typing.

The order lands in the store and someone has to turn it into an invoice, adjust stock, create the contact and apply the right tax. We build the AI layer between the WooCommerce API and the Holded API that runs that whole trip — including the cases no connector covers.

See solution

Connecting Stripe to Holded is the easy part. Making the books add up isn't.

The charge goes through. The invoice, the credit note, the fee and the net payout are another story: someone ends up squaring them in a spreadsheet at month end. We build the AI layer between Stripe's API and Holded's that does that work on its own.

See solution

A connector links Shopify to Odoo. Keeping the odd order from breaking your warehouse is another matter.

The happy path —product published, order imported, stock pushed— any connector syncs. What jams is the partial refund, the bundle that is three items inside Odoo, the order the customer changes afterwards and the webhook that one day never arrives. We build the AI layer that handles that, and we ship it to production.

See solution

Orders come in over WhatsApp and someone types them into Odoo by hand. That gets connected.

Integrating WhatsApp with Odoo isn't forwarding messages: it's an order, a query or a payment that arrives over WhatsApp ending up created in the right Odoo module, without anyone typing it.

See solution

Connecting Google Drive to ChatGPT is the easy part. Getting it to answer only with what that person can open isn't.

Your team already did it: they paste the contract into their personal account because it's faster than digging through Drive. We ship the sanctioned path —the model reads your Drive with the permissions of whoever is asking, cites the document behind every answer and leaves a record of all of it— so nobody has to choose between moving fast and leaking the company.

See solution

Integrating HubSpot with Slack is the easy part. Getting the CRM to update itself without muting the channel isn't.

Your team sells in Slack and then, if there's time left, writes it up in HubSpot. We build the integration the other way round from how it usually gets built: HubSpot updates from what happens in Slack, and Slack only speaks when there's a decision to make. No notification channel nobody reads.

See solution

Putting ChatGPT in a cell is a demo. Automating Excel with AI is a process nobody has to run.

We are not here to install an add-in. The problem is not that Excel is missing an AI formula: it is that the same file lands every week and somebody sits down to classify it row by row. We build that work as a process —the sheet goes in, comes out done, with the doubtful rows flagged and a record of why— on your Microsoft 365, and we ship it to production.

See solution

AI Operations

Your AI provider is going to go down. The question isn't whether — it's what your operation does for that half hour.

Monitoring tells you something is failing. Availability is what keeps the work shipping while it fails: a second path, a degraded mode, queues that absorb the hit and a recovery target agreed per use case. We build that function and we run it.

See solution

The model you bought is going away. The only question is whether you find out before your customer does.

Picking a model isn't a one-time decision: providers retire versions on notice, update them underneath you and move prices, and your system stays in production through all of it. We build the function that turns switching models into a boring operation — your own test bench, real comparison, and a way back — instead of a leap of faith.

See solution

Training your team to run AI isn't a ChatGPT course. It's building them a function.

With AI in production there are three roads: hire the team, have someone else run it, or have the people you already employ run it. The third is the fastest to start and the worst executed, because it gets mistaken for a one-day workshop. We design the function, train your people on your real systems, and stay until they hold it up on their own.

See solution

Your prompts are in production and nobody knows for sure which version is running

The prompt is the single biggest lever on how your AI behaves, and in most companies it gets edited by hand: no review, no history, no way back. We build and run the function that turns it into a versioned artifact, tested before it ships and reversible in a minute.

See solution

Every AI agent you run has a switch-on date. Almost none has a review date or a switch-off date.

Building an agent is a project. What almost nobody has built is the function that governs it afterwards: a catalog where every agent has an owner and a version, a gate that decides which change reaches production, a rollback that works, a review that checks whether it still earns its keep, and a real retirement — credentials revoked. We build that lifecycle and we run it.

See solution

Your AI decides every day. Six months from now, can you explain one specific decision?

A log is not a trace. Having records scattered across the model, the tool and the destination system does not let you reconstruct why this request was denied, this price was applied or this case closed on its own. Decision traceability is the function that ties every decision to its input, its versions, its data, its policy and its approver —and keeps it retrievable when someone asks. We build it and we run it.

See solution

Your AI agent doesn't need to make a mistake to do damage. Someone outside just has to talk to it.

Agent governance decides what yours is allowed to do. Security decides what someone who doesn't work here can get it to do: an instruction hidden in an email the agent reads, a tool nobody audited, a secret riding along in the context. We build that layer —isolation, identity, filtering and forensic trace— and we run it.

See solution

You have several AI agents. What you don't have is a system.

Once every agent runs on its own, the problem stops being the prompt and becomes the hand-off: who does what, in what order, with what context, and who answers when the chain breaks. That orchestration layer gets built and run. It does not get improvised.

See solution

Human oversight of AI at scale: the function that puts a person at the exact point of thousands of decisions —without slowing the volume

Putting a human to review every AI output doesn't scale; removing them entirely buys you an incident. Human oversight at scale isn't "someone watching the screen": it's an operational function —sampling, review queues, escalation of the doubtful, approval of the irreversible— that decides where a person steps in and where they don't, and holds it up when you go from one agent to a hundred.

See solution

Evaluating the quality of your AI agents: the function that measures, watches and corrects them in production — before the customer feels the error

An agent that passed the demo isn't an agent that works in production: it degrades on its own when the model, the prompt or the world changes, and nobody notices until a customer complains. Evaluating its quality isn't a test you run once; it's a function that runs — sample, score against a rubric, catch regressions and correct — so quality stops being an impression and becomes a number you watch.

See solution

Monitor AI in production: the function that watches your agents live — latency, cost, failures and drift — and alerts you before the customer

An agent in production doesn't fail with a red error: it fails in silence — slows down, spikes the cost per token, starts returning garbage when a tool changes — and without observability you don't find out until someone complains. Monitoring AI isn't a pretty dashboard nobody looks at; it's the function that instruments, watches and alerts: traces of every call, live metrics, alerts when something goes off-script and whose phone rings at 3am.

See solution

Build an AI Operations team without improvising halfway through

You've decided AI stops being a loose experiment and becomes a function. Now the hard part: which roles, in what order, who leads it, and how it's measured. That gets designed — not improvised one job req at a time.

See solution

Build AI Operations in-house, or have us run it?

The question isn't "AI or not." It's who runs the function: you hire and train an internal team, you outsource it entirely, or you go hybrid. Each option has a real cost and a point of no return. We help you decide with numbers, not faith.

See solution

Hiring an AI Operations Manager: what to ask for, what to pay, and what your alternatives are

It's the role that turns AI pilots into daily operation. But the title is inflated and the salary bands are all over the place. Before you open the req: what the role actually does, what it costs in your market, and whether hiring is your best move today.

See solution

AI Operations as a service: who runs your AI in production every day, not just on launch day

This isn't automating one more task. It's the whole function: someone watching that your AI systems keep working, fixing what drifts, and responding when something breaks — on an ongoing basis, without you building an internal team for it.

See solution

From AI pilot to production: the function that crosses the chasm where 95% of pilots die —and keeps them alive after

The pilot isn't the problem; the leap to production is. That leap isn't a final push, it's a continuous function: industrializing the pilot, making it perform on real data and keeping it alive. We run it for you, so your pilot doesn't join the statistic of the ones that stayed a demo.

See solution

Govern your company's AI agents: the control layer that decides what they can do, keeps a record of everything, and responds when one goes off the rails

You have AI agents scattered across several teams, acting on real systems. The question is no longer whether they work: it's who governs them. What they're allowed to touch, who approves the sensitive stuff, what gets audited, and what happens when one drifts. That isn't a panel you watch; it's a continuous function you run. We build the governance layer and put it to work.

See solution

You've got AI agents doing things and you no longer fully control what. Before one causes a mess, here's what to do to rein them in —and we leave it in place.

You started with one agent that helped, then another, and now several are acting on real systems —sending emails, changing data, spending on model calls— and you're less and less sure what each one can do or what would happen if one overstepped. It's not paranoia: an agent acting on its own doesn't warn you it's gone rogue, you find out once it's already done something. Before governing anything, you have to stop the immediate risk: least-privilege permissions, human approval on what costs money or touches people, and a real kill switch. We put that in place now; and on top, the stable control so it doesn't happen again.

See solution

Who maintains your AI agents once they're in production?

The agent got built, the demo went well and today it runs on its own. Until it drifts, a model changes, an integration goes down or the cost spikes — and nobody's job is keeping it working. That's the gap. You close it with a function, not a support ticket.

See solution

Scale from 5 to 100 AI agents without each one bringing its own fire

One person can watch five agents by eye. Not a hundred. At that scale the problem stops being building agents and becomes running them: orchestrating, supervising, controlling cost, and containing the incident before anyone notices. That layer gets built — not improvised once you've got eighty running.

See solution

Audit your processes for AI: know what's a fit, in what order, and at what risk before you automate anything

Automating the wrong process is expensive, and automating blind is worse. Before you run AI you need to know which processes are a fit, which aren't, and in what order to bring them in — by volume, cost, variability, criticality, and available data. That audit isn't a slide deck you hand over once: it's a capability run continuously, because your processes and the models shift every quarter.

See solution

Enterprise AI integration: not just connecting AI to your systems, but keeping that layer alive when everything shifts underneath

Connecting AI to your ERP, your CRM and your data is day one. The real bill arrives on day two: the provider changes the model, an API updates, a permission expires, and the integration goes down in silence. Enterprise AI integration isn't a project you deliver and forget; it's a layer you operate —versioned, monitored and governed— so the AI keeps talking to your systems when the ground moves.

See solution

AI agent incident management: the function that responds when something breaks in production — triage, on-call, rollback and postmortem — not when the customer complains

Monitoring detects that something is wrong; someone has to respond. Without an incident-management function, every agent failure in production is an improvised scramble: nobody knows who responds, what severity it is, whether to roll back or ride it out, or why it happened again the next week. This isn't putting out a fire one night; it's the function that runs — severities, on-call, runbooks, rollback, root cause and corrective actions — so an AI incident gets resolved fast and doesn't repeat.

See solution

What does it cost to run your AI in production? The bill you didn't see coming — and the function that keeps it under control, month after month

You budget for building AI once. You pay to run it every month, and that bill grows on its own: what was cents in the demo becomes thousands a month, split across five vendors nobody can break down. Controlling the cost of running AI isn't squeezing spend one afternoon — it's a function that runs: measure, attribute, budget, optimize and govern, so the AI line stops being a month-end surprise.

See solution

Comply with AI regulation while running your AI: classify risk, document and keep conformity a living function, not a PDF you sign once

AI compliance isn't won with a consultancy and a report that goes in a drawer. Your AI systems change model, data and use every month, and conformity has to move with them: classify each one by risk, document it, put oversight where the rules require it, keep evidence and manage incidents. If you sell into the EU, that's the EU AI Act; in the US it's the emerging frame — the NIST AI Risk Management Framework, state laws and your sector's regulator. Either way it's not a project that ends; it's an operation you keep. We build it and we run it.

See solution

Keep your AI's knowledge current: the function that stops your assistant answering with last year's policy

An AI is only as good as the knowledge it pulls from, and that knowledge expires on its own. The base you built on day one drifts from reality — a price changes, a policy, a procedure — and the assistant keeps answering with the old version. Keeping it current isn't "re-uploading the files" now and then: it's an operating function that detects what changed, re-ingests just that, validates and invalidates stale answers, with an owner and a cadence per source.

See solution

Want it running in your business?

You’ve pinned the problem. We ship the fix and leave it measured.

See the service
AI solutions by problem · Implementa