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Hours back every week
Sorting email, keying in invoices, matching payments and compiling reports happen in the background, so your people spend their time on the work that needs them.
AI that takes real work off your plate
We build AI features into products, and automations that run parts of your operations: triaging inboxes, reading invoices, reconciling payments, writing reports. Tested on your real data, with a person in the loop wherever a mistake would cost you.
100+ startups, enterprises and agencies trusted us with their projects.
How we work
- 01
Map the manual process
We sit with the people doing the work and map it step by step: what comes in, which decisions get made, where errors creep in and what an hour of it costs. Some steps need AI. Many just need good software.
- 02
Prototype with real data
Early on, you see a working prototype run on a sample of your real emails, documents or records, so you judge it on your own cases, not on a demo.
- 03
Evaluate
We build a test set from real examples and measure accuracy before anything goes live. Every change to prompts, models or code is checked against it.
- 04
Roll out with monitoring
We roll out gradually, often with a person approving each result at first, then automate the cases the system handles reliably. Every run is logged, and costs, errors and outcomes are monitored.
- 05
Iterate
We review failures and edge cases regularly, grow the test set, and tune prompts, tools or models as your process and the models themselves change.
Why it's worth it
Automation you can trust with real work
The goal isn't AI for its own sake. It's fewer repetitive hours, fewer mistakes, and systems that behave the same on a busy Monday as they did in the demo.
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Fewer errors
Data read from documents is validated against rules and your existing records, so a mismatch gets flagged instead of copied into your books.
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Tested before it ships
Every system comes with a test set built from your own examples. We know how well it performs before launch, and we see when a change makes it worse.
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People review what matters
Guardrails limit what the system is allowed to do, and uncertain or high-stakes results go to a person to approve. You decide where that line sits.
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No lock-in to one model
The code talks to every model through one standard layer, so switching between Claude, OpenAI and others is a setting, not a rewrite.
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Works with the tools you use
Automations connect directly to your inbox, CRM, accounting software, spreadsheets and databases, instead of adding one more tool to check.
Models, data and hosting
The right model for each job, running where it fits
The Vercel AI SDK gives the code one way to talk to every model. Behind it, we pick a model per task and run the system where it suits your data and budget.
Vercel AI SDK
One layer in the code that talks to every model, so switching models stays simple.
Models and memory
Claude
Anthropic's models. Often our first pick for agents, tool use, long documents and careful writing.
OpenAI
When a GPT model tests better for the task, and for embeddings, speech and image input.
Neon
A Postgres database that also holds AI search data, right next to the rest of your data.
Supabase
Database, sign-in, storage and AI search in one, when the feature lives inside a Supabase app.
Runs on
Cloudflare
Runs background jobs and retries, and tracks what every model call costs.
Vercel
When the AI feature lives in a Next.js app already deployed on Vercel.
Google Cloud
When your data already lives in Google Cloud, or a job runs for a long time.
What we can do
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Inbox triage
Incoming email sorted, labeled, routed and given a draft reply, with anything unusual sent to a person.
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Invoice and document ingestion
Supplier invoices, receipts and forms read, validated and entered into your accounting software or ERP.
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Reconciliation
Payments matched to invoices and orders across your bank, Stripe and accounting, with the leftovers listed for review.
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Reporting
Weekly and monthly reports compiled from your tools and written up in plain language.
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CRM enrichment
New leads and accounts researched and filled in, so sales starts every conversation with context.
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Support assistants
Answers drafted from your docs and past tickets, with a clear hand-off to a person.
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AI features in your product
Assistants, search that understands meaning, and tools that read and sort documents inside your app, with usage-based billing on Stripe if you sell them.
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Your systems in AI assistants
Claude and other AI assistants connected to your own systems through MCP, the standard connector for AI tools, with permissions you control.
Frequently asked questions
What happens to our data, and what gets sent to model providers?
Only the data a task needs is sent, and personal details can be masked before anything leaves your systems. We use the providers' business services, which under their current terms don't train on your data by default, and we check retention and region options against your requirements before we build. Where data can't leave, we look at running models in your own cloud.
What does it cost to run?
Models are billed by usage, so the running cost depends on volume, document size and the model. During the prototype we measure the real cost per task on your data, and keep it down with the smallest model that passes the tests, caching and batching. You see the numbers before you commit.
How accurate is it, and who checks the output?
No AI system is right every time, and we won't promise otherwise. We measure accuracy on your own examples before launch, set confidence thresholds, and send uncertain or high-stakes results to a person. You see the error rate and decide what runs fully on its own.
Which model do you use?
Whichever does the job best at the lowest cost, tested on your data. Often that's Claude, sometimes OpenAI or another model, and sometimes different models for different steps. The code isn't tied to one provider, so switching later is simple.
Do we actually need AI for this?
Not always. Plenty of repetitive work is solved with plain software: an integration, a rule, a scheduled job. We'll tell you when that's the better answer, and use a model only for the steps that need judgment or language.
How long does it take?
A prototype on your real data usually comes within a few weeks. A production rollout depends on how many systems are involved and how much review you want at the start. We scope it after a discovery call.
Who builds and maintains it?
Mihai, the founder, designs the architecture and builds the core himself, with senior partners joining where the project needs them. You deal directly with the person who knows how the system works, and the code, prompts and test sets live in your repository.
What happens after launch?
Models, processes and data change, so AI systems need looking after. We offer ongoing monitoring and improvement, or a documented handover to your developers with the test set to check any change.
Related services
- App developmentSaaS products, client portals and internal tools, with sign-in and billing set up properly from day one.
- Supabase developmentApps, client portals and products on Supabase, with sign-in and data access designed properly.
- Ecommerce appsB2B portals, custom checkouts, subscriptions and marketplaces, built around how you sell.
Your next big move starts with a solid foundation
Whether you need a new app, a website redesign or a long-term technology partner, we’re ready to talk.