- Industries / AI Startups
AI Startups —MVP
to scaled AI product.
LLM platforms, AI agents, RAG systems, and AI copilots — engineered for fast launch, hard evals, and scalable infrastructure.
What's slowing
ai startups down.
The operational and experience bottlenecks we keep seeing across the sector — and what we re-architect first.
Rapid MVP development
Founders need a working product in weeks, not quarters — without sacrificing eval rigor.
AI infrastructure complexity
Vector stores, eval harnesses, prompt management, and observability are a stack on their own.
Scalability issues
Demos work; production traffic with cost limits and latency budgets is a different game.
Integration challenges
AI products live or die by how cleanly they slot into customer workflows and data sources.
High development costs
Token costs, infra costs, and engineering costs all need disciplined attention from day one.
Who we
build for.
The teams and end-users our work in this sector serves day-to-day — each with their own constraints and incentives.
/01
AI founders
Technical founders shipping fast, iterating with users, and proving value before fundraising.
- we build for them
Startup teams
Small teams owning everything from prompts to product — needing leverage and senior judgment.
- we build for them
AI product companies
Post-PMF AI companies scaling reliability, evals, and customer success motions.
- we build for them
Venture-backed startups
Series A/B companies expanding from one workflow to a platform — without losing focus.
- we build for them
Products we
build for healthcare.
Platform types we’ve shipped — each tailored to the regulations, workflows, and stakeholders of the sector.
/01
AI SaaS platforms
End-to-end AI products with billing, auth, evals, and observability built in from day one.
- Multi-tenant
- Evals
- Observability
/02
LLM integrations
Provider-agnostic LLM layers with routing, fallbacks, caching, and cost controls.
- Multi-provider
- Caching
- Cost guards
/03
AI copilots
In-app assistants grounded in tenant data with citations, tool-use, and clear hand-off paths.
- RAG
- Tool-use
- Citations
/04
AI automation systems
Agentic workflows with human-in-the-loop checkpoints, retries, and audit trails.
- Agents
- HITL
- Retries
/05
Intelligent workflows
Adaptive pipelines that route work between models, tools, and humans based on confidence.
- Routing
- Confidence scoring
- Fallback
Intelligence baked
into every flow.
Where AI lifts margin, accelerates delivery, or removes manual ops — applied with judgment, never for show.
- AI capability
Generative AI
Text, image, voice, and structured generation tuned for your domain and brand voice.
- AI capability
AI agents
Multi-step agents with tool-use, planning, and bounded autonomy — built with eval guardrails.
- AI capability
RAG systems
Production-grade retrieval pipelines with hybrid search, chunking strategy, and freshness controls.
- AI capability
Workflow automation
AI-native automation engines that orchestrate models, tools, and humans across a business process.
- AI capability
Predictive intelligence
Forecasting and anomaly models that turn AI from a chat surface into a quantitative product.
What changes
after we ship.
The measurable shift our clients see — operationally, commercially, and in the eyes of their customers.
60%
- Time-to-MVP
Reusable AI infrastructure and senior team move v1 from quarters to weeks.
50%
- Token cost
Caching, model routing, and prompt optimization meaningfully cut per-request cost.
99.9
- Production uptime
Eval-gated deploys and provider fallbacks keep AI products online at SaaS-grade SLA.
3×
- Iteration speed
Eval harnesses and observability let teams ship prompt and model changes safely, daily.
5×
- Customer integrations
Provider-agnostic LLM layers and integration patterns make customer onboarding linear.