Copilots, retrieval and intelligence engineered into your existing product, including replacing the third-party AI you currently license.
- AI features built inside your product
- RAG grounded in your customers’ data
- License replacement with owned capability
GenAI and agentic products engineered end to end: the platform, the models, the experience and the operations behind them.
- Full product engineering, GenAI to go-live
- Agentic architectures with human oversight
- Memberships, payments and integrations built in
Flows rebuilt as conversations: an LLM guiding users through dynamic Q&A instead of marching them through forms.
- Conversational LLM experience design
- Preference capture inside the flow
- Automated completion end to end
Models tuned to your domain and behavior: fine-tuning, synthetic data and evaluation, at the standard frontier AI products demand.
- Fine-tuning and distillation for your domain
- Human-centered model behavior engineering
- Evaluation frameworks run continuously
AI-driven test generation across your product surface, so release confidence grows with the codebase instead of shrinking.
- End-to-end test architecture
- AI test generation for deeper coverage
- Automation woven into release pipelines
Platforms and AI workloads tuned for your margins: architecture, inference cost and observability engineered together.
- AI workload and inference cost optimization
- Cloud architecture tuned for unit economics
- Observability on every AI decision path
Product AI · For a Compliance SaaS Leader · AI Engineered Into a SaaS Product
The Challenge
- AI capability rented through a costly third-party license
- No internal store of the rules the product runs on
- Business-critical content slow to produce
Our Approach
- A canonical rules library, structured plus vector
- 100+ sources tracked, changes diffed and versioned
- Agentic course generation grounded in the rules
Outcomes
Foundation Models · AI Product Engineering · Human-Centered AI Model Engineering
The Challenge
- AI products demanding more than raw model intelligence
- Human-centered behavior hard to engineer at model level
- Third-party API integrations to support at scale
Our Approach
- Human-centered models combining EQ with IQ
- Engineering contributions at the frontier of conversational AI
- Third-party API integrations built alongside
Outcomes
Agentic Product· Travel Tech · Conversational Booking Platform
The Challenge
- Booking flows built on forms, not conversations
- User preferences captured slowly, step by step
- Reservation complexity pushed onto the user
Our Approach
- A conversational LLM guiding users through dynamic Q&A
- Agentic AI capturing preferences instantly
- Automated flight and hotel booking end to end
Outcomes
Production-Grade Delivery
Human in the Loop, Always
Unit Economics as a Design Input
Production, Not Pilots
Discover
Design
Build
Run & Scale
Both work; the honest difference is time to shipped. We engineer alongside your team and hand over, so you end up with the capability in-house either way.
Model choice, caching and inference cost are decided during design, so the feature’s cost per use is known before launch, not discovered on the first invoice.
Yours. We build in your repositories and your accounts, and ownership transfers with the code.
Yes. We work inside your codebase, and our code goes through the same reviews and gates yours does.
The architecture assumes it will: model-agnostic design plus an evaluation harness, so swapping a model is a benchmark run, not a rewrite.
