Custom AI Solutions

AI built into your product, not bolted on.

Yoursa built-and-owned AI asset wired to your data, not a rented feature

When an off-the-shelf tool won't cut it, we build the custom thing — RAG assistants over your data, AI features inside your product, model integrations and internal tools — designed, built and wired into your stack.

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Overview

Sometimes the use-case is yours alone. A support assistant that has to reason over ten thousand of your documents. An AI feature your users would pay for. An internal tool that turns a specialist's afternoon into a click. No SaaS subscription fits, because the value is in your data, your workflow, your product. That is where a custom build earns its keep.We design and build AI solutions end-to-end. Retrieval-augmented (RAG) assistants that answer accurately over your private knowledge. AI features embedded in your app via the OpenAI, Anthropic and open-source model APIs. Internal tools that automate expert judgement. We handle the architecture, the prompt and retrieval engineering, the guardrails and evaluation, and the integration into the systems you already run, with the same taste for craft and performance we bring to everything.The result is a durable asset, not a rented feature. It is built around your data and your workflow, owned by you, and designed to be maintained and extended as you grow. Because brand, web and AI sit under one roof here, the thing we build looks and feels like your product, not a grafted-on widget from a different world.

—What you get

Solution design and technical scoping, with a build-or-buy call before a line of code
RAG assistant or AI feature built over your data, docs or product
Model integration (OpenAI, Anthropic, open-source) via secure, monitored APIs
Prompt, retrieval and guardrail engineering, with evaluation to keep answers reliable
Integration into your app, site or internal tools, and your data sources
Documentation, monitoring and a handover so your team can run and extend it

Why it works

Grounded in your data

RAG means the AI answers from your actual documents and data, with citations, accurate and specific, not a generic model guessing. The value no off-the-shelf tool can match is that it knows your world.

Engineered, not prompted

Real products need evaluation, guardrails, fallbacks and monitoring, not a clever prompt and hope. We build for reliability and cost, and test against real cases before it ships.

Looks like your product

Design and engineering under one roof means the AI feature feels native, your brand, your UX, your standards, instead of a bolted-on widget that breaks the experience.

—How we work

01
Scope
We pin down the use-case, the data it needs and what 'good' looks like, and give you an honest build-or-buy call before committing to a build.
02
Prototype
We build a working proof over your real data fast, so you can see quality and cost on your actual use-case before we invest in the full thing.
03
Build
We engineer the solution, retrieval, prompts, guardrails, evaluation, and integrate it into your product or tools, built for reliability and sane running costs.
04
Ship & support
We deploy with monitoring, document how it works, and hand it over, with the option to keep improving it as your data and needs grow.

FAQ

Good questions.

What's RAG, and why does it matter?+
Retrieval-augmented generation lets an AI answer from your specific documents and data instead of only what it learned in training. It is the difference between an assistant that actually knows your product, policies and history, with citations, and a generic model that guesses. For most business use-cases it is the piece that makes AI trustworthy.
Which models do you build on?+
Whatever fits the job, OpenAI and Anthropic for capability, open-source models where privacy or cost demands it. We are model-agnostic and design so you are not locked to one provider if the landscape shifts.
Is our data safe?+
Yes, we architect for it. Your data stays in systems you control, we use providers' no-training options and secure APIs, and for sensitive cases we can build on models that run in your own environment. Security and privacy are part of the design, not an afterthought.