Frame
We define the task, the evals, and what 'good' looks like before building.
AI & Automation
AI Product Development Claude, GPT and open models built into real products.
AI product development means shipping AI as a dependable product feature, not a demo. We integrate Claude, GPT, and open-source models into real applications with fine-tuning, prompt engineering, RAG pipelines, and AI-native UX that users actually trust.
How We Deliver
What working with us on this looks like in practice.
AI Product Development Claude, GPT and open models built into real products.
We map the system before we touch it
Codebase, pipeline and risk assessed in week one.
Decisions written down, not assumed
Stack, boundaries and quality gates agreed up front.
Short sprints, working software
Every sprint ends with something running you can try.
Suites that gate every merge
Parallelised runs keep regression in minutes, not days.
You see what we see
Coverage, escape rate and MTTR on a live dashboard.
What's included
RAG Pipelines & Retrieval
Retrieval-augmented generation over your private data, tuned for accuracy and latency, not just a demo.
Prompt Engineering & Evals
Structured prompts and automated evaluation harnesses so every change is tested against a real benchmark.
Model Fine-Tuning
Fine-tuning and deployment of open-source or hosted models, matched to your task, cost, and latency needs.
AI-Native UX
Interfaces designed around AI's real failure modes, streaming, graceful degradation, and user trust.
Applications
What you can ship with AI product development.
Custom RAG chatbots. Ground a chatbot in your own docs, tickets, and product data with a RAG pipeline, so answers are cited and accurate instead of generic model completions.
AI-native features inside your product. Summarization, smart search, and recommendation surfaces built with Claude or GPT integration and shipped as first-class product features, not a bolted-on widget.
Internal AI tools for your team. Internal copilots that answer from your company's own knowledge base, support macros, sales enablement, and engineering runbooks your team actually uses daily.
Evals before you ship. Prompt and RAG evaluation pipelines so every release is measured against a real accuracy target, not a demo that happened to work once.
AI enablement for your product team. We train your product and engineering teams to prompt, evaluate, and iterate on AI features themselves, so you're not dependent on us for every change.
Who it's for
Product teams shipping AI
You need AI features that actually work, not demos.
Series A–C startups
AI is the wedge, so it has to be defensible and evaluable.
Enterprises with private data
RAG, evals, and guardrails so you can trust it in production.
Definitions
AI product terms, defined
The concepts that decide how an AI feature is scoped, built and proven.
- RAG (retrieval-augmented generation)
- RAG is an architecture where relevant documents are retrieved from your own data and passed to the model, so answers are grounded in your facts rather than the model's memory.
- Evaluation set
- An evaluation set is a fixed collection of real inputs with agreed-correct outputs, used to score every prompt or model change objectively.
- Guardrail
- A guardrail is a control enforced in application code around the model, so it holds even when the model does not comply with its instructions.
- Fine-tuning
- Fine-tuning is further training of a model on your examples to change how it behaves, such as output format or tone, rather than what facts it knows.
| Approach | Solves | Ongoing cost | When to choose it |
|---|---|---|---|
| Prompt engineering | Vague or inconsistent output | Effectively none | Always try this first |
| RAG | Wrong or outdated facts | Ingestion and retrieval upkeep | Knowledge changes or needs citation |
| Fine-tuning | Format and tone consistency | Retrain on each base-model change | High volume, one narrow task |
| Agents | Multi-step tasks that vary | Tool maintenance and monitoring | A fixed workflow cannot cover it |
Ready to build ai product development?
Tell us what you're building. We'll come back with a scoped plan and a fixed first milestone.
Start a project
How we work
01 02 03
A clear path from idea to production.
Build
Retrieval, prompts, and UX iterated against a real evaluation set.
Harden
Guardrails, monitoring, and cost controls so it holds in production.
Outcomes
What "done" looks like.
<200 ms retrieval latencyFast outcome
95%+ eval-set accuracyMeasured outcome
Hallucinations caught pre-shipProven outcome
FAQ
AI Product Development, answered.
Which models do you use?
We are model-agnostic, Claude, GPT, Llama, Mistral, chosen per task on quality, cost, and latency.
How do you stop hallucinations?
Retrieval grounding, strict prompts, and automated evals that gate every change against a test set.
Can you work with our private data?
Yes, via RAG and secure vector stores, your data stays yours and is never used to train base models.
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