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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
Integrate AI into products with guardrails, observability, and workflows that create real business value—not demos.
Teams we build with
AI demos impress stakeholders but fail in production—hallucinations, cost spikes, and no observability erode trust.
How we approach it
LLM features with guardrails, evaluation harnesses, and cost controls built into your existing product and release process.
How we deliver
We treat AI as a product surface: define policies, measure quality, and ship behind flags with rollback paths.
Scope varies by engagement—these are the capabilities we most often deliver on projects like yours.
LLM feature development
RAG pipelines and knowledge bases
Agent workflows and automation
Evaluation and monitoring
Typical technology stack
Map scope, milestones, and team shape in one call.
Contact usSystem design
A typical stack for this practice—adapted to your compliance, cloud, and team constraints.
Stack layers
Narrower layers are closer to the user · wider layers are platform depth
Layer 1 · Experience
AI surfaces users trust—streaming responses, edit-and-resubmit, citations, and clear loading and error states.
Components & tools(9)
Repeatable practices that keep quality high across milestones—not one-off heroics.
Ground answers in your knowledge base with freshness controls.
Tool use with timeouts, retries, and audit trails.
Regression tests on prompts before every release.
Token budgets, caching, and model routing by task.
Quality gates
Non-negotiable quality gates we apply before every release—not a post-launch checklist.
6 checkpoints on typical engagements
Standard 1: Never ship without offline eval baselines
Standard 2: Log prompts and outputs with PII redaction
Standard 3: Provide citations or confidence where users decide
Standard 4: Human review for high-risk actions
Standard 5: Plan model upgrades without breaking UX
Standard 6: Gate releases behind eval score thresholds—not demo quality alone
Common questions about ai development engagements.
When you need production AI—not a demo—with clear guardrails, observability, and a path to integrate with your existing product and data stack.
Timelines depend on scope and integrations. We define phased milestones in week one—typically a discovery sprint, build cycles with demos, then hardening and launch support.
We embed with your product and engineering leads through shared roadmaps, async updates, and structured reviews. You keep ownership of the codebase and infrastructure.
A 30-minute discovery call, then a short technical assessment and proposal with scope, team shape, and risks—no lengthy RFP process unless you need one.
30-minute call. We'll tell you if we're the right team—and what we'd do in the first two weeks.