AI your customers use, built into your product.
Chat, search, recommendations and copilots that feel native to your app, with the evals, cost controls and monitoring that keep them good after launch.
What we ship
Features that lift activation and retention.
- 01
In-app copilot
An assistant that understands the user's data and can take actions on their behalf.
- 02
Semantic search and Q&A
Retrieval over your docs, catalog or content, with cited answers.
- 03
Recommendations
Personalised suggestions that adapt to behaviour, not just rules.
- 04
Smart content tools
Summaries, drafts, tagging and extraction inside your editor or workflow.
- 05
Voice and multimodal
Upload an image or speak a request, and get a structured result.
- 06
Private and on-device
Models in your cloud or on the device when data cannot leave your control.
What you get
The feature, shipped
Built into your product's own UI and codebase, in your conventions.
Evals and quality gates
Automated checks that block a release when quality drops.
Cost and latency budget
Caching, model choice and fallbacks tuned so the feature pays for itself.
Monitoring and feedback capture
Dashboards and user feedback loops so you see failures before users complain.
Docs and handover
Architecture notes and a walkthrough for your engineers.
The patterns we reach for
Features inside your product need predictable behaviour, so we favour fixed designs and measure them.
Evals first, demos second
An AI feature is only as good as the examples it is tested on. We build the test set with you before we write the feature.
From idea to a feature in production
A typical path for one feature. We confirm yours in the written plan.
- Stage 1
Define the metric
Agree what good looks like and build the test set with you.
- Stage 2
Build the feature
Backend on the Austial framework, client in the stack you already run.
- Stage 3
Evaluate and tune
Score quality, latency and cost, then tune models and prompts.
- Stage 4
Launch and monitor
Staged release with dashboards, alerts and handover docs.
What we measure
- Adoption
- How many active users reach for the feature, and how often.
- Answer quality
- Scored on your real data by evals, plus thumbs and edits from users.
- Latency
- Time to first useful output, held to a budget we agree up front.
- Unit cost
- Model spend per use, so margins stay visible as usage grows.
Who this is for
Product teams adding search, copilots or generation
You know the feature and need it to survive real traffic.
Founders racing a competitor's AI launch
A narrow, well-evaluated first version beats a broad flaky one.
Engineering leads short on AI experience
We build alongside your team and leave the knowledge behind.
One AI feature in your product: design, build, evals and launch.
Iterate on the feature, add new ones and keep model cost in check.
Frequently asked questions
Which stack do you build in?
Python on the Austial framework for backends, and TypeScript on the client, including React Native. We match what your team already runs where we can.
Can the feature use our own data safely?
Yes. Retrieval respects your permissions, and customer data stays in your cloud under your retention rules.
How do we control model costs?
Caching, smaller models where quality allows, and per-customer caps, with a cost dashboard from day one.
Do you work inside our codebase?
Yes, in your repository and conventions, through pull requests your team reviews, so nothing arrives as a black box.
How do you handle our customers' data?
We agree what may leave your systems before building, minimise what is sent to model providers and keep prompts and logs inside your environment where possible.
What if the model changes or gets worse?
The eval suite is the safety net. We run it on every change and when you switch models, so regressions show up in a report rather than in production.