Use-case spike
Fixed spike against real data and workflows — with eval scores and cost estimates before a full build.
ADD AI
Generative AI Solutions
LLM-powered assistants, copilots, RAG search, and agentic workflows wired into your real data.
Overview
How we approach generative ai solutions.
Generative AI demos impress boards and disappoint operators when retrieval is wrong, costs spike, or answers can’t be evaluated. We start from a real workflow, data contracts, and success metrics — then build copilots that live inside the tools people already use.
Production means eval harnesses, prompt and version control, observability for latency and cost, and guardrails for safety. If it can’t be measured and rolled back, it isn’t ready for your customers or your staff.
The point
AI features that earn trust in real workflows — not a demo that dies after the board meeting.
Deliverables
Concrete AI systems, not just demos.
Every engagement leaves your team with production-shaped AI features, evaluation harnesses, and the guardrails to run them safely.
- Use-case roadmap with success metrics and cost targets
- RAG or agent architecture with data contracts
- Evaluation suite for quality, safety, and regressions
- Production copilots wired into your existing tools
- Observability for latency, cost, and answer quality
- Runbooks, prompt/version control, and handover notes
Engagement Model
Prove one use case, ship a production copilot, embed an AI pod, or keep eval and ops care.
Production copilot
Ship a scoped RAG or agent feature with guardrails, logging, and a rollback path.
AI delivery pod
Engineers and eval specialists embed to expand use cases without reinventing the platform each time.
Eval & ops care
Ongoing regression evals, prompt changes, and cost/latency tuning as models and content shift.
Timeline
From use-case proof and evals to a guarded production feature.
Week 1
Use cases, data, and success metrics
Pick workflows worth automating, map data sources, and define quality, safety, and cost targets.
Weeks 2–3
Architecture and eval harness
Stand up retrieval or agent paths, baseline prompts, and an evaluation suite that can catch regressions.
Weeks 4–7
Product integration and hardening
Wire into existing tools, add observability and guardrails, and run red-team and cost reviews.
Launch
Limited rollout and handover
Phased release, feedback loop, prompt/version ownership, and runbooks for incidents and model changes.
What we need from you
Data access and workflow owners that make AI useful.
Clean source systems and a human who knows the job beat a generic “add ChatGPT” brief.
- Access to knowledge sources with clear retention and PII rules
- Workflow owner who can judge answer quality in context
- Cost and latency ceilings leadership will actually enforce
- Security/compliance constraints for model and data residency
- Existing product surface where the copilot will live
Common mistakes we help avoid
Generative AI mistakes that burn trust and budget.
We design engagements to make these hard to ship by accident.
- Launching a chatbot with no evaluation suite or regression tests
- Ignoring retrieval quality and blaming “the model” for bad answers
- No cost observability until the invoice arrives
- Skipping guardrails for PII, jailbreaks, and unsafe actions
- Building a standalone demo instead of wiring into real tools
FAQs
Answers before we get on a call.
Do you build on a specific model vendor?+
We choose models and hosts that fit data residency, cost, and quality — and keep the architecture swappable where it matters.
How do you know the AI is “good enough”?+
Agreed eval sets, human review samples, and production metrics for usefulness — not vibes from a single happy-path prompt.
Can you work with our private documents?+
Yes, with contracts for access, retention, and redaction. RAG designs respect what must never leave your boundary.
What about agents that take actions?+
Actions need explicit permissions, audit logs, and human-in-the-loop where risk is high. We don’t ship unbounded tool use.