01 / Software products
Software products that support real operations
Customer-facing and internal products spanning interfaces, backend systems, data, integrations, billing, and deployment.
03 / Applied AI
AI-Enabled Product Engineering & Automation
Practical AI features built around real workflows: LLM integration, RAG, semantic search, tool-using agents, and human-reviewed automation.
Product lanes: Software products that support real operations · AI that fits a measurable product workflow
Book a free consultation ↗Product context
Where this service fits in the product system.
02 / AI-enabled products
AI that fits a measurable product workflow
AI features, retrieval, agents, and automation designed around evaluation, permissions, human review, cost, and operational fallback.
Lifecycle coverage
The engagement stays connected to the stages around it.
Scope can begin at any listed stage. Earlier decisions and later operating requirements remain visible so the work does not become an isolated technical deliverable.
01
Discover
Clarify the user, painful workflow, desired change, evidence, constraints, and decision owner.
02
Scope & architecture
Define the smallest valuable release, system boundaries, risks, milestones, and acceptance evidence.
03
Design & prototype
Make the critical experience and uncertain assumptions testable before committing to the full build.
04
Build & integrate
Deliver the interface, backend, data, AI or device connections in reviewable product slices.
05
Verify & launch
Test the important paths, deployment, failure states, security boundaries, and operating readiness.
06
Handover & iterate
Document decisions and operations, transfer ownership, measure the result, and plan the next release.
What the engagement can produce
Final outputs are narrowed during discovery, but the engagement can cover these product outcomes when the scope requires them.
- AI use-case and data-readiness assessment
- Provider-agnostic LLM integration
- RAG, embeddings, vector search, and evaluation flows
- Tool-using agents with permission boundaries
- Review queues, feedback loops, cost controls, and monitoring
How this engagement is shaped
- 01
Define the decision or task AI should assist, including failure costs.
- 02
Prototype against representative data and establish a baseline.
- 03
Add evaluation, observability, fallbacks, and human approval points.
- 04
Integrate into the existing product and monitor quality and cost.
Evidence and relevant decisions
Review related case studies, public experiments, and practical engineering guidance before deciding whether the fit is right.
Local LLM Workflows
A public experiment exploring local inference, privacy, latency, hardware, and OpenAI-compatible integration tradeoffs.
Review the local-model experimentReliable AI features need evaluation and review
A practical guide to retrieval, agents, evaluation, fallbacks, and human approval in product workflows.
Read the AI reliability guideReview-gated AI commerce operations
Product evidence for configurable model routing, persisted drafts, approval boundaries, and auditable outputs.
Read the applied AI case studyImplementation toolkit
Technology follows the product boundary, existing system, operating constraints, and handover needs. These are relevant tools, not a prescribed stack.
LLM APIsRAGEmbeddingsVector searchMCPOllamaTypeScriptPython
Common questions
Do you build AI agents?
Yes, when an agent is justified by the workflow. For deterministic tasks, a simpler automation is usually cheaper, faster, and easier to operate.
Can the system use a local model?
Yes. Local or OpenAI-compatible runtimes such as Ollama can be evaluated when privacy, latency, hardware, and model quality make that tradeoff sensible.
How do you reduce hallucinations?
The design combines scoped context, retrieval, structured outputs, validation, explicit uncertainty, evaluation datasets, and human review for consequential actions.
Next step
Start with the outcome, current stage, and most expensive uncertainty.
Send the current context, desired change, timeline, and budget range through the product brief.