01Step 1 of 4
We pressure-test the idea first
A short feasibility pass. If AI is the wrong tool for it, you hear that before you spend.
AI DevelopmentReasoned
Everyone says you need AI. Nobody says what for.
Most AI projects fail because they start from the technology instead of a task worth automating. We start from the workflow - the thing your team does forty times a week - and build only if it holds up. Sometimes the honest answer is that you don't need a model at all.
We build AI features into products where there's a specific task worth automating, and we start by checking whether there is one. The projects that fail tend to begin with the technology and go looking for a use; the ones that work begin with a task a team repeats often enough to measure.
Fixed priceagreed before we start
- Sound familiar?
- Your team spends hours on work that looks automatable.
- You've tried an AI feature and it produced confident nonsense.
- You're sitting on documents or data nobody can search properly.
Deliverables
What you get.
- 01Use-case scoping & feasibility check
- 02RAG pipelines over your own documents
- 03LLM features built into an existing product
- 04Workflow & document automation
- 05Evaluation harness and quality guardrails
- 06Cost, latency & rate-limit monitoring
Tools & approach
- Claude API
- OpenAI API
- LangChain
- pgvector
- Pinecone
- Python
- Next.js
The work
How this runs.
02Step 2 of 4
A prototype on your real data
Demos on sample data prove nothing. We test against the messy version you actually have.
03Step 3 of 4
Measure before shipping
We build an evaluation set so 'is it good enough?' has an answer rather than an opinion.
04Step 4 of 4
Ship with the costs visible
Token spend, latency and failure rates monitored from day one, not discovered on an invoice.
Detail
Worth knowing first.
Does your business actually need AI?
Sometimes not, and that's a real answer rather than a polite one. If the task is rule-based and consistent, ordinary software does it more cheaply, more predictably and without a per-token cost.
AI earns its place on work that involves language or judgement at volume: reading unstructured documents, summarising, classifying messy input, answering questions across scattered material, or drafting something a person then edits. If your problem doesn't look like that, we'll say so at the feasibility stage rather than after you've paid for a build.
How do you stop an AI feature from inventing answers?
Grounding is the main mechanism. Rather than asking a model what it knows, you retrieve the relevant passages from your own documents and ask it to answer from those - then show which source each answer came from, so anyone can check it.
The second mechanism is measurement. We build an evaluation set of real questions with known good answers and run it on every change, so quality is a number that moves rather than a feeling. Without that, nobody can tell whether last week's prompt edit helped or quietly made things worse.
What does it cost to run, not just to build?
AI features carry an ongoing per-use cost that traditional software doesn't, and it scales with usage rather than sitting flat. Getting this wrong is the most common unpleasant surprise.
We model expected cost per request during the prototype, choose model sizes to match the difficulty of the task rather than defaulting to the largest, cache aggressively where answers repeat, and monitor spend from day one. Not every step needs a frontier model, and using a smaller one where it performs identically is most of the saving.
Is your company data safe, and does it train anyone's model?
On the standard business API tiers we build on, inputs are not used to train the provider's models. We confirm the terms for whichever provider your project uses and put the specifics in writing rather than asking you to take it on trust.
Beyond the provider's terms, the architecture matters: we keep sensitive fields out of prompts where they aren't needed, retain only what the feature genuinely requires, and can keep retrieval entirely within your own infrastructure where the data justifies it.
Questions
Answered plainly.
Any language model can. The mitigation is grounding answers in your own sources, showing where each answer came from, and letting it say it doesn't know. We build the evaluation set that measures how often it gets things wrong, so it's a number you can see rather than a risk you're guessing at.
Start with ai development.
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Get in touch
Start with ai development.
Tell us what you're working on and what's slowing you down. A short call is usually enough to know whether we're the right fit - no proposal required first.
