Our services
Applied AI
Practical AI pipelines — RAG, agents, streaming LLM workflows.
Start a projectOverview
We build AI features that reach production, not demos. The work covers retrieval over your own content, agents that call real tools, and streaming LLM workflows wired into the stack you already run: TypeScript, Next.js, Postgres with pgvector, queues, edge and node runtimes. We treat prompts, models and retrieval as code — versioned, covered by evals, observable request by request. It fits teams that already have data, users and an operating cost to justify — support, documents, internal search, onboarding — and need the AI layer to be reviewed and tested like the rest of the system.
What it covers
RAG over your own content
Ingestion, chunking, embeddings and hybrid search across your documents and databases. Answers arrive with citations to the source, so whoever reads them can check where each claim came from.
Agents with real tools
Tool calling against your APIs, with typed schemas, retries, timeouts and a human approval step wherever the action is irreversible. The scope is declared in code: an agent can only call the tools you gave it.
Streaming interfaces
Token-by-token responses with server actions and edge runtime, with cancellation, partial state and reconnection. The interface stays usable while the model is still writing.
Evals and regression tests
A golden set built from your real cases, run in CI on every change to a prompt, a model or the retrieval layer. Each version is compared against the previous one instead of assumed to be better.
Document and data extraction
Parsing, OCR and structured extraction with schema validation, so model output lands in typed fields instead of loose text. Anything that fails validation goes to a review queue.
Cost, latency and routing
Model routing per task, prompt caching, batching, and per-request traces of tokens, latency and spend. Cost becomes something you can query per feature and per request, not a line you read on the monthly invoice.
How we work
- 01
Frame the use case
We start with the task, not the model: what comes in, what output is acceptable, who reviews it. We write the acceptance criteria with your team and rule out whatever does not need an LLM.
- 02
Prototype on real data
A minimal end-to-end version of the flow, running against your actual documents and traffic. Real data surfaces the retrieval and formatting problems that a curated demo hides.
- 03
Harden the pipeline
Evals, guardrails, fallbacks, rate limits and observability. We set cost and latency budgets, and make failures visible and recoverable instead of silent.
- 04
Ship and hand over
We deploy inside your infrastructure with CI/CD, dashboards and a runbook. Your team keeps the repository, the evals and the documentation, and can keep iterating without us.
What you get
- Production pipeline deployed in your own infrastructure, source code included
- Streaming API endpoints, with their documentation
- Eval suite with a golden set built from your own cases
- Prompts, model and retrieval configuration versioned in the repository
- Tracing of tokens, latency and cost per request
- Runbook and technical handover session
The AI stops being something one person runs on a laptop and becomes a feature with an owner, tests and a cost you can read. When a model, a prompt or a document set changes, you can measure the effect before shipping it. And because the logic lives in your repository and not in a vendor's console, switching providers is work you do in code you already own.
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