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AI Development Company for Products That Need to Work in Production

We build LLM features into new and existing software: answers grounded in your own documents, data extraction, drafting and in-app assistants. Each feature ships with an evaluation set, so you can see how often it gets things right before your users do.

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Where we help

Where AI projects usually stall

A demo takes an afternoon. Getting the same feature to behave on real data, at a cost you can afford, is the work we do.

  • The prototype works on ten examples

    It looked great in the demo and fails on real customer data. We build a test set from your actual inputs and improve the feature until it passes.

  • Answers are made up

    The model answers confidently from nothing. We ground it in your documents with retrieval, show the sources it used and make it say when it does not know.

  • Costs and latency are out of control

    Every request goes to the largest model and users wait ten seconds. We route simple tasks to smaller models, cache repeated work and set a cost budget per request.

  • You are not sure where AI fits

    The board wants an AI roadmap and the team has a list of ideas. We help you pick the one or two use cases with clear value and data you already have, and say plainly when a rule or a form would do the job better.

What we build

AI features we build

Each one is wired into your product, logged and measured.

  • Search and Q&A over your documents (RAG)

    Assistants that answer from your help center, contracts, policies or product data and cite the passages they used.

  • Document and data extraction

    Pulling structured fields out of invoices, forms, emails and PDFs into your database, with confidence scores and a review queue for unclear cases.

  • Drafting and summarization

    First drafts of replies, reports and listings in your house style, which a person reviews and sends.

  • In-app assistants

    Chat or command interfaces inside your product that can look up the user's own data and take simple actions with their permission.

  • Classification and routing

    Tagging tickets, leads and documents and sending them to the right queue, so your team stops sorting by hand.

  • Evaluation and monitoring setup

    A test set built from your data, automated scoring and dashboards for quality, cost and latency, so model upgrades are a measured decision.

Process

From idea to a measured AI feature

We prove the feature on your data before building the full product around it.

See how we work

  1. Use-case review

    We look at the task, the data available and what a wrong answer would cost. You receive a short note on whether AI fits and which approach we recommend.

  2. Evaluation set

    We collect real examples with the correct answers, agreed with your team. You receive the test set and a clear pass mark for the feature.

  3. Prototype on your data

    We build the smallest version and run it against the test set. You receive a working prototype and its scores, including the cases it gets wrong.

  4. Production build

    We add the interface, permissions, logging, fallbacks and cost limits, then release. You receive the feature in your product and a monitoring dashboard.

  5. Tune and hand over

    We review real usage, fix failure patterns and document how to re-run the tests when models change. You receive the runbook and the evaluation code.

Tech stack

Models and tools we use

We are model-agnostic and choose per task based on your test results, cost and data rules.

Models
ClaudeGPTGeminiLlamaMistral
Frameworks
Vercel AI SDKLangChainLlamaIndexModel Context Protocol
Retrieval
pgvectorPineconeQdrantElasticsearch
Evaluation and observability
LangfusepromptfooOpenTelemetry
Hosting
Amazon BedrockGoogle Vertex AIMicrosoft Azure

AI development FAQ

How much does it cost to add AI to an existing app?

It depends on the feature and how clean your data is. We usually start with a short prototype on your data, which tells us both whether the feature works and what a full build will take. After that you get a fixed-scope quote.

Will our data be used to train AI models?

We use the business API tiers of model providers, which do not train on your inputs by default, and we confirm the current terms for the provider you choose. If your data cannot leave your cloud, we can use models hosted in your own AWS, Azure or Google Cloud account.

How do you stop the AI from making things up?

We ground answers in your own content, show the sources, and test the feature against real questions with known answers. We also design the interface so a person reviews anything where a wrong answer is costly.

Which model should we use?

The one that passes your test set at the lowest cost and latency. We run the same evaluation across a few models and show you the results, and we keep the code model-agnostic so you can switch later.

How long until we have something working?

A prototype on your own data usually takes two to three weeks, including the evaluation set. A production feature takes longer depending on integrations and review steps.

When should we not use AI?

When the task has clear rules, when every answer must be exactly right with no review step, or when you have no examples of good output. In those cases ordinary code or a simple form is cheaper and more reliable, and we will tell you so.

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Next step

Talk to us about AI Development

Book a free 30-min call. Tell us what you need and we will suggest a practical first step, or tell you honestly if we are not the right fit.

Book a free 30-min call