What we build

AI features for products that have users, or soon will. Every one ships with tests and monitoring, so you can see how it performs.

Four kinds of workLLM integrations, agents, search over your documents, and evals and monitoring.

And the software around themAPIs, admin screens, dashboards and deployment.

Start a project

Four services

Most projects mix two or three of them. A support assistant, for example, is an LLM integration that searches your documents and is watched by evals.

LLM integrationsChat, search and writing help inside your product
AgentsSoftware that finishes a task with your tools
Document searchAnswers from your own files, with the sources
Evals and monitoringTests and tracing that keep quality up

LLM integrations

Chat, search, summaries and writing help, built into the product you already have.

The feature uses your data, respects your users’ permissions and stays inside the cost you set. It answers in your product’s voice, in the languages your users write in, and it says so when it does not know.

Most of the work is not the prompt. It is choosing the model, deciding what context the model may see, streaming the answer into your interface, and keeping a record of what was asked and what was said.

What this includes

  • Prompt design, with every version kept and tested
  • Choosing and routing between models
  • Cost limits per user and per feature
  • Streaming answers in your interface
  • Privacy and data-handling rules
  • Usage and quality analytics

More about LLM integrations

Agents

Software that works through a task step by step, and passes the case to a person when it should.

An agent reads a request, decides which of your tools to call, checks the result and carries on until the task is done or a rule says stop. It can file a ticket, look up an order, draft a reply or update a record, but only the things you allow.

Every step is logged, so you can see what it did and why. When it is unsure, or when the stakes are high, it hands the case to a person with everything it found so far.

What this includes

  • Tool and function calling against your APIs
  • Clear states, retries and timeouts
  • Hand-off to a person, with the context
  • An audit trail of every action
  • Limits on what the agent may do
  • Tests for the paths that matter

More about agents

Evals and monitoring

A model that worked last month can drift this month. Evals and monitoring tell you when it does.

We build test sets tied to the goals you care about and run them on every change: a new prompt, a new model, a new batch of documents. In production we trace each request, so a drop in quality or a jump in cost shows up before your users notice it.

We often start here when a feature is already live and nobody can say whether it is getting better or worse.

What this includes

  • Test sets built from real cases
  • Automatic checks on every change
  • Safety and policy filters
  • Tracing and cost tracking
  • Gradual rollouts and quick rollback
  • User feedback fed back into the tests

More about evals and monitoring

The software around it

An AI feature needs a normal product around it. We build that too, so you do not need a second team to make the first one useful.

Ask about your stack

APIs and back ends

Node.js and Laravel services that hold the business rules, the permissions and the data your AI feature needs.

  • REST and webhooks
  • Queues and background jobs
  • Login and permissions

Admin screens

The screens your team uses to review answers, fix the index, change prompts and see what the feature costs.

  • Review queues
  • Prompt and settings screens
  • Usage and cost views

Front ends

The part your users touch: chat panels, streaming answers, source links and feedback buttons, in React or Vue.

  • Streaming interfaces
  • Accessible markup
  • Feedback capture

Data connectors

Links to the places your knowledge lives: file shares, wikis, ticket systems, CRMs and databases, kept in sync.

  • Sync jobs
  • Access rules
  • Change tracking

Deployment

CI/CD, containers and the cloud you already use, with logs, alerts and backups set up before launch.

  • Docker and Kubernetes
  • Build and release pipelines
  • EU hosting when needed

Tools we use often:

OpenAI and Anthropic models, direct or through Azure and AWS Bedrock; open models in your cloud; TypeScript, Python, Node.js, Laravel, React and Vue; PostgreSQL, pgvector, Qdrant and Redis; Docker and Kubernetes.

We are not tied to any vendor. On each project we test more than one option, pick the one that fits your cost, speed and data rules, and write down why.

Where to start

All questions

We know the problem, not the technique. Which service do we need? Tell us the problem. If your users ask questions your documents already answer, it is usually document search. If people repeat the same multi-step task in several systems, it is usually an agent. If the feature exists and nobody trusts it, it is evals and monitoring. We will say which, and we will say so if none of them fits.

Can we start small? Yes, and we recommend it. A first version that does one thing well, with its tests, teaches more than a large plan. Most projects begin with one feature for one group of users.

Does our data have to be clean first? No. Most data is messy, and part of our work is finding out which mess matters. In the first week we look at real samples and tell you what has to be fixed and what the feature can live with.

Can you take over a feature someone else built? Yes. We start by measuring it: we build a test set from real cases, run it, and show you where it fails. Then you decide whether to fix it or rebuild it.

Can you work inside our own cloud account? Yes. We can build and run everything in your AWS, Azure or Google Cloud account, so the data and the bills stay with you.

Do you only build chat? No. Chat is one shape among many. Plenty of useful AI features have no chat window at all: a summary at the top of a ticket, a field filled in for you, a document sorted into the right folder.

Not sure which one?

Tell us the problem instead. We will say which approach fits, and whether AI is the right tool for it at all.

A few lines are enough. We reply within one working day.

Start a project