Evaluated before launch
Every feature ships with tests that show how well it performs on your cases.
- Task-specific evaluation sets
- Human review rounds
- Regression checks on model changes
- Quality dashboards
Generative AI
built into your product.
We help you find where generative AI earns its place, then build it into your own software — copilots, generation features and the models behind them, tested and governed.
Generative AI is easy to demo and hard to ship. We focus on the part after the demo: quality, cost and trust.
What we deliver
From choosing the first use case to running models in production, scoped to the value you expect.
- 01
Strategy & use-case discovery
Workshops with your teams to list where generative AI could help, then rank each idea by value, risk and effort.
- 02
Copilots & chat in your product
Assistants and chat features built into your app or platform, so your customers and staff get help where they already work.
- 03
Content & code generation
Features that draft copy, summaries, reports, images or code from your templates and data, with review steps before anything goes out.
- 04
Model selection & fine-tuning
We compare hosted and open models on your own tasks, then tune prompts, fine-tune or distill where it improves quality or lowers cost.
Generative AI you can stand behind
A feature that works on stage has to keep working on real users, real data and a real budget.
Responsible AI & governance
Clear rules for what the model may see, say and do, written down and enforced.
- Usage policies & risk review
- Content filtering
- Privacy & PII handling
- Audit trails
Cost and speed under control
Model choice, caching and routing tuned so the feature stays fast and affordable as usage grows.
- Model routing by task
- Prompt & response caching
- Token and spend tracking
- Latency budgets
From idea to production feature
- 01
Discovery
We map your goals, data and users, and agree on the one or two use cases worth building first.
- 02
Proof of value
A working prototype tested on your real inputs, with clear measures of quality and cost.
- 03
Build & integrate
The feature built into your product, with guardrails, evaluation and monitoring in place.
- 04
Launch & improve
A staged rollout, then regular tuning as models change and user feedback comes in.
You ask. We answer.
How is this different from your AI automation service?
AI automation is about agents and workflows that run your internal processes. Generative AI is about building AI features into your own products, choosing and tuning the models, and setting the rules for how AI is used.
Which model should we use?
It depends on the task, your data rules and your budget. We test a shortlist of models on your own examples and recommend the one that gives the best balance of quality, speed and cost.
Will our data be used to train public models?
No. We use providers and settings that keep your data out of their training, and we can deploy in a private cloud or with open models you host yourself.
Do we need to fine-tune a model?
Often not. Good prompts and retrieval cover most needs. We fine-tune only when testing shows it clearly improves quality or cuts cost.
Ready to build with generative AI?
Tell us what you want your product or team to do better, and we'll show you where generative AI fits.