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AI

AI that does a jobinside your product.

Not a chatbot bolted to the side. Agents, retrieval and automation that earn their place, built model-agnostic and measured before they ship.

Who this is for

Teams with a real task, not a slide about AI.

A workflow to automate

Documents to process, tickets to triage, data to extract, decisions to draft. Work with a clear input and a checkable output.

A product feature

Search that understands, assistants that act, generation that stays on brand. Built into the product you already have.

Research before commitment

You suspect AI could change your economics and want a prototype and a straight answer before betting the roadmap on it.

What we build

From prototype to production, with the measurement in between.

Most products need two or three of these. We'll tell you which, and which would be a waste.

01

Agents and workflows

Multi-step systems that use your tools and data, with guardrails, human checkpoints and clear failure modes.

02

RAG and knowledge assistants

Retrieval-augmented generation over your documents and data: grounded answers with citations and access control, as a product feature or an internal copilot in Slack or Teams.

03

Embeddings and vector search

Semantic search across your datasets, products or support history. Embedding pipelines, a vector store that fits your stack (often just Postgres), and relevance you can measure.

04

Fine-tuning and custom models

When prompting stops being enough: open-weight models tuned on your data for tone, format or a narrow task, hosted where your data is allowed to live.

05

Document processing and extraction

PDFs, emails, forms and scans turned into structured, validated data your systems can act on. The unglamorous work with the fastest payback.

06

Evaluation and guardrails

Test sets, scoring and monitoring so you know how well it works before and after launch, not from anecdotes.

07

Model-agnostic architecture

AI SDK or Mastra as the harness, so you can change providers when the price or quality changes. It will.

08

Cost and latency engineering

Caching, routing between models, batching, smaller models where they're enough. The difference between a feature that scales and a bill that doesn't.

09

Agent-ready products

MCP servers and tool APIs so other people's agents can use your product safely. The next integration surface, built with auth, limits and audit from the start.

10

Voice and multimodal

Speech in and out, call and voice agents, image and document understanding. Real-time where latency matters, batch where it doesn't.

11

Private and self-hosted inference

Open-weight models on your own infrastructure or in your region, for data residency, predictable cost or both. Sized and operated by our infrastructure team.

12

AI security and compliance

Prompt-injection defence, red-teaming your agents, and the audit trail and documentation an EU AI Act or enterprise review will ask for.

How we work

Experienced engineers in charge of the model, never the reverse.

01

Define the job

What goes in, what should come out, and how we'll know it's right. If that can't be written down, it isn't ready to build.

02

Prototype against real data

Days, not months. Your documents, your edge cases, your users' phrasing.

03

Measure, then harden

An evaluation set before production. Guardrails, fallbacks and monitoring around whatever the model gets wrong.

04

Ship inside the product

In your codebase, on your infrastructure, with the same review and ownership as everything else we build.

FAQ

Questions we get about AI work.

Straight answers to what comes up on every first call. If yours isn't here, ask us.

Whichever fits the task and the budget, behind an abstraction that lets you switch. We'll recommend a default and explain why, and we don't take vendor incentives.

Got an idea?Tell us about it.

Send a short brief. We'll come back with questions, a call slot and a straight answer on whether we're the right team.

Start a project