01
Feature design
We define what problem the AI solves and how it's measured.
APIAI inside
AI inside
your product
We integrate language and vision models directly into your software — with the reliability, latency and traceability production demands.
What it is
Turn your product into
an AI experience.
If you sell software, your next competitive edge is AI inside the product: automatic summaries, suggestions, generation, search — turned into real features for your customers.
We integrate language models (Claude, GPT-4, Gemini) and vision (multimodal) into your product via API. We take care of what matters in production: low latency, caching, cost control, fallbacks and observability so you get no surprises.
- ✓Picking the right modelBy capability, price, latency and availability.
- ✓Robust prompt designVersioning, evaluation and guardrails included.
- ✓Real productionCaching, retries, observability and cost control.
- ✓Multi-model & fallbackDon't depend on a single provider. Automatic switching.
How we do it
From idea to deploy,
in 4 steps
A lightweight, iterative process with tangible deliverables from the very first week.
02
Prototype & evaluation
We test several models and prompts against real cases.
03
Production integration
We put it in your code with the observability and security needed.
04
Monitoring
Latency, cost, quality and model drift, watched live.
Impact
Results you can measure
Reference figures from similar projects. We validate yours in the first phase.
−60%
Cost per request
With caching, the right models and tuned prompts.
<400ms
Median latency
Streaming and caching for a lively UX.
99.9%
Uptime with fallback
If one provider goes down, another responds without the user noticing.
Use cases
Real applications by industry
B2B SaaS
Summaries & extraction
Turns long documents into actions inside your app.
Marketplaces
Semantic search
So users find what they mean, not what they type.
Education
Adaptive tutor
Explanations and feedback tailored to each student.
Health
Clinical assistant
Draft reports with source citations and human review.
Productivity
Content generation
Drafting, translation and rephrasing inside your UI.
Computer vision
Image analysis
Classification, OCR and multimodal description in production.
Stack
Technologies we use
We pick tools to fit the project, not the other way around. These are the ones we reach for most.
Claude (Anthropic)GPT-4 (OpenAI)Gemini (Google)OpenAI Vision · Claude VisionStreaming · SSELangfuse · HeliconeCloudflare · Vercel · AWS
FAQ
Frequently asked questions
Which model should we use?
It depends on the case. We usually test 2–3 models against your data and choose by quality, latency and cost.
What if the chosen model gets pricier or goes down?
We design multi-model from day 1: automatic switching if one fails or gets more expensive.
How do you control costs?
Aggressive caching, streaming, the right models per task and per-user limits. We measure everything.
Is it stable for production?
Yes. We implement retries, timeouts, fallbacks and observability before going to real users.
Who owns the prompt and the logic?
You do. Everything stays in your repository, with your team. We transfer knowledge from the start.
Next step
Turn your software
into an AI product.
We propose the first AI feature in your product and take it to production with you. We show you the prototype before committing to anything.
Other products