AI Development Company for real products.
We build production AI apps — not demos. LLMs, RAG, agents and automations that actually make money.
Model-agnostic
OpenAI, Anthropic, Gemini, open-source — we pick the model that fits the use case and budget.
Production-ready
Rate limits, cost controls, eval harnesses, observability. Not a Jupyter notebook.
Full-stack in one team
Frontend, backend, infra and AI in one crew — no vendor hand-offs.
Process
A calm, product-led way of working.
Step 01
AI audit
We map where AI adds real value in your product or workflow — and where it doesn't.
Step 02
Prototype the hardest part
We validate the AI feature first on real data before building the app around it.
Step 03
Build and integrate
Ship the AI feature inside your product with proper auth, billing and analytics.
Step 04
Measure and tune
Eval sets, cost dashboards, and monthly tuning to keep quality high and cost low.
Services
Packages built for outcomes, not hours.
Pick a package or build a custom scope in the calculator.
mvp
Startup MVP
From idea to real users in weeks.
3–6 weeks
- Product definition
- MVP scope and roadmap
- UX and design system
- Frontend + backend + DB
- Auth, roles and payments
automate
Business automation
Cut manual work, connect your tools.
2–4 weeks
- Process audit
- Internal tool or dashboard
- Integrations (CRM, email, Sheets)
- n8n / Zapier flows
- Notifications and alerts
webapp
Web application
A product with real users, auth and data.
2–4 weeks
- Product architecture
- UX and design system
- Frontend + backend
- Database and auth
- Roles and permissions
Typical delivery
Typical delivery
Discover
1–3 days
Design
3–7 days
Build
1–4 weeks
Launch
1–3 days
FAQ
AI development — common questions
Which models do you use?+
Whichever fits — GPT-4/5, Claude, Gemini, Llama, Mistral. We benchmark for your use case.
How do you keep costs under control?+
Prompt caching, model routing, response caching and per-tenant rate limits — built in from day one.
Do you handle RAG and vector search?+
Yes — pgvector, Pinecone, Qdrant. We pick based on data volume and latency targets.
Can you build AI agents?+
Yes. Tool-using agents with guardrails, retry logic and human-in-the-loop review where it matters.
How do you evaluate AI quality?+
Golden datasets, LLM-as-judge and real-user feedback loops. We ship dashboards you can read.
Have an AI idea?
Book a call. We'll tell you honestly whether AI actually helps here — and how to build it.