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Production AI systems and digital engineering for enterprise teams.Explore our work
Production AI systems and digital engineering for enterprise teams.Explore our work
We partnered with a B2B SaaS team to design, build, and launch LLM-powered workflows with production guardrails and measurable adoption metrics.
Overview
A B2B SaaS platform serving operations teams needed to ship AI-assisted workflows without pausing their existing roadmap. Leadership had board pressure to show AI value in-quarter, while engineering worried about hallucinations, cost runaway, and support load. We joined as a product engineering partner—embedding with their squad, not running a parallel demo track.
Industry
B2B SaaS
Duration
16 weeks
Engagement
Product + AI engineering
Team
4 engineers + PM
The product already had strong core workflows; AI was meant to accelerate repetitive tasks inside those flows—not replace them. The team had experimented with prompt prototypes in a branch, but nothing was wired to permissions, audit logs, or their release process. Security required tenant isolation on every retrieval path, and customer success needed citations when the model answered from knowledge base content.
What we delivered
We designed an orchestration layer that sat behind their existing API gateway: typed tool calls into product data, RAG over synced documents, and a policy engine for input/output filtering. Features rolled out behind flags with offline eval baselines and a pilot cohort before general availability.
Phased delivery with clear acceptance criteria at each step.
How we deliver
We worked in two-week vertical slices—each ending in a demo on production-like data. Product, design, and engineering signed off on acceptance criteria before we expanded scope to the next workflow.
Tangible outputs the client team owned at handoff—not slide artifacts.
LLM gateway with quotas, schema validation, and audit logging
Document ingestion and embedding pipeline with tenant isolation
Copilot UI with streaming, citations, and edit-and-resubmit
Offline eval suite tied to CI and release checklist
Runbooks for on-call, model upgrades, and incident response
Executive readout with adoption metrics and phase-two roadmap
Outcomes
Measured impact from the program—not projected estimates.
Faster time-to-market
vs. internal estimate for the same scope
Uptime post-release
30-day window after GA
Workflow adoption
Active use in target journeys vs. pilot baseline
Cost per workflow
Blended inference at steady-state volume
Technologies
What we'd do again
More proof
Explore programs in other industries—or view the full portfolio.
Tell us about your product and timeline—we'll share how we'd approach it.