AI Services
AI built into the software you already run.
Copilots, agents, and document intelligence — designed around your data, integrated with your product and workflows, and run in production. It starts by finding what's worth building.
2-week sprint · fixed price · 100% credited toward implementation
What we build
AI Discovery Sprint
Two weeks to map where AI creates measurable ROI in your software. The front door to everything below.
AI Agents & Assistants
Copilots and autonomous agents wired into your product, your tools, and your permissions — supervised, evaluated, and improved in production.
Document Intelligence & Retrieval
Contracts, invoices, tickets, and internal knowledge turned into answers and actions — grounded in your own data, not the open internet.
Computer Vision
Detection, counting, and quality control on real-world images and video, integrated with the systems that act on the results.
Responsible AI
Guardrails, evaluation, and human oversight designed into every system — so what ships stays accurate, auditable, and under your control.
What every system carries
Every system we ship starts supervised: a person approves what it proposes. That is not a limitation to apologise for; it is how the system learns the edge cases and how your team learns to trust it. Each correction becomes a rule. When corrections approach zero for one kind of action, the gate opens for that kind — and stays closed for the ones that still surprise you.
Grounding
the system retrieves from your data before it acts, and its outputs point to where they came from. When your data doesn't contain the answer, "I don't have that" is the correct response, and the system says it.
Scope
an explicit list of the systems and actions it may touch, enforced by your permissions, not by a prompt.
Evaluation
a test set of real cases, run every time a prompt, a model, or the data changes. "It works" gets a number before we build.
Audit trail
every action traceable to the input, the model, and the rule that allowed it.
This is the shape we run on our own operations, and the shape we build into every client's.
Proof
We built Frankie, Amgen's voice assistant for field sales reps — their data, by voice, while they drive to the next visit. In production since 2019, before the LLM era. Our own hiring: an agent that turns interview transcripts into client-ready profiles has run our hiring since early 2025 — behind a human gate first, every correction a rule, and a year in production since.
View case study →Sophilabs presented us with a product that we were proud to show people.
From the field
How engagements run
It starts by finding what's worth building. A fixed-price sprint with the senior engineers who would build your system: you leave with prioritized use cases, honest feasibility, and a roadmap you can execute with us or without us — and sometimes the honest answer is "not yet." Most teams continue into a fixed-price implementation of the first use case, shipped by the same engineers who scoped it; if you already know exactly what to build, skip the sprint and bring the use case straight to implementation. The build starts narrow on purpose: one workflow, one team, one metric — a first agent or document flow is weeks of work, not quarters. From there, many keep building with us on a monthly subscription priced on output, not hours. Either way, the roadmap includes the numbers, so what comes next is a decision, not a surprise.
FAQ
Can you add AI features to software that already exists and has users?
Yes. That is what we do: copilots, assistants, document intelligence, retrieval and computer vision, built into products already running in production with real users and real constraints.
How do you work inside our permissions and data model?
We use them as they are. An AI feature that ignores your permissions model is a security incident waiting to happen, so retrieval and actions respect the same rules your product already enforces, per user and per tenant.
Which models do you use, and who chooses?
Commercial, open-source or custom, chosen for the use case rather than the logo. We bring the trade-offs — cost, latency, data residency, quality on your task — and you decide. The architecture keeps the model swappable, because the right answer today is rarely the right answer in a year. In practice: a commercial model through its API for most copilots, an open-weight model on your own infrastructure when data can't leave your environment or volume makes per-call pricing untenable, and classic machine learning when the input is a table.
How do you measure whether an AI feature worked?
With a number agreed before the work starts — hours saved, errors avoided, time to complete a task — measured against how the same work was done before. If a feature can't be tied to a number, that is a sign it isn't the one to build first.
What can our data support today?
That is one of the four questions the discovery sprint answers. Most teams have more than they think for retrieval and less than they think for training; knowing which is which before building is the difference between a feature that ships and a pilot that stalls. Where it can't support a use case yet, that is a finding, not a failure: the preparation it needs can be the first step of the roadmap.
How do you handle data that can't leave our environment?
By not moving it. The system runs inside the access you grant — your cloud, your permissions, your audit log. Where a commercial model is the right tool, we use terms that exclude your data from training; where that isn't enough, open-weight models run inside your environment.
Who maintains it after launch?
Your team, if you want: everything ships with the evaluation set, the runbook, and the learned rules. Many clients keep building with the same engineers on a monthly basis, priced on what ships, not hours; either way, the hand-off is written to work without us.