AI-Driven SaaS Engineering
Cloud-native SaaS platforms with LLMs, cognitive workflows, and semantic search embedded from day one.
Who this is for
Built for the right stage.
- Post-seed to Series-B teams turning AI features from demo to durable product.
- Data-heavy operators who need retrieval, not just prompts.
- Product leaders shipping AI where hallucinations, cost, and latency actually matter.
Deliverables
What you take home.
LLM orchestration
Prompt architecture, tool use, and multi-model routing with cost + latency budgets.
Vector + hybrid search
pgvector or managed store, fused with lexical scoring for real retrieval quality.
Grounded generation
Citation contracts and refusal policies so answers are trustworthy by design.
Multi-tenant foundation
Isolation, quotas, and per-tenant analytics built into the core.
Usage metering & billing
Per-token and per-feature metering wired into Stripe.
Eval + regression harness
Automated eval runs on every deploy so quality never drifts silently.
Admin console
Internal tooling for prompts, guardrails, and tenant support.
Observability
Traces, cost telemetry, and quality dashboards from day one.
Timeline
Four phases. No mystery.
Discover
Use cases, data sources, and evaluation criteria locked.
Design
Retrieval architecture, prompt system, and product surface designed.
Build
Weekly demos across ingestion, retrieval, generation, and product.
Launch
Beta cohort, observability, and iteration playbook.
Tech stack
Chosen for velocity — and to last.
AI
Data
Backend
Infra
Investment
What's included in the engagement.
- Full 6–12 week engagement with senior AI + product engineer
- Retrieval + generation architecture, tuned to your data
- Eval harness so quality can be measured and improved
- Cost + latency dashboards in production
FAQ
The common questions.
Do you use OpenAI or open-source models?
What about hallucinations?
Can we swap models later?
How do you handle sensitive data?
Next step