Thinking through the build.
Practical perspectives on AI engineering, company knowledge, and the decisions that make a product useful.
Strategy & investment
Scope a useful first project, choose a partner, and understand the investment.
Turn an AI readiness assessment into a roadmap
A readiness assessment becomes useful when it identifies the first workflow, its blocking gaps, accountable owners, and a measurable implementation decision.
3 min readConsulting decisionsHow to hire an AI consultant for a project that ships
A practical shortlist and proposal checklist for founders choosing an AI consultant: workflow scope, evidence, delivery ownership, and handover.
4 min readProject investmentWhat drives the cost of an AI development project?
Estimate an AI project around integrations, source preparation, evaluation, and operating costs instead of treating a chatbot demo as the whole budget.
3 min readNairobi & global deliveryAI consulting in Kenya: scope a useful first project
A Nairobi-based engineer's practical guide for Kenyan SMEs and global teams choosing a first AI workflow, establishing integration access, and planning delivery.
4 min readAgents & automation
Design workflows with measurable outcomes, approval boundaries, and recovery paths.
Does your business need an agent or an automation?
A practical way to choose between predictable workflows and AI agents, using uncertainty, tool access, and approval boundaries as the deciding factors.
3 min readWorkflow discoveryAn AI workflow automation audit you can act on
Map triggers, manual work, exceptions, systems, and approval boundaries to choose a first automation with a clear business owner.
4 min readAgent reliabilityHow to evaluate a production AI agent before launch
Test an agent's tool use, approvals, retries, recovery, and task completion with a scenario matrix instead of judging only its final answer.
3 min readKnowledge & MCP
Connect company information to AI with sources, permissions, and review.
Production RAG starts with evidence, not embeddings
How to separate retrieval quality, access control, source provenance, and answer evaluation when building a useful company knowledge system.
3 min readMCP integrationsConnecting company knowledge to AI clients through MCP
What Narravo's MCP integration illustrates about scoped context, tools, authentication, and keeping suggested learning separate from approved knowledge.
3 min readArchitecture decisionsRAG vs fine-tuning: choosing for company knowledge
Decide whether your application needs fresher evidence, more consistent behavior, or both before investing in retrieval or model fine-tuning.
3 min readCompany knowledgeWhen a company knowledge graph helps an AI assistant
Use relationships, provenance, and approval states when company questions need more than similar document passages, without assuming every RAG system needs a graph.
4 min readIntegration boundariesMCP integration security: what to check before connecting
A practical review of MCP identities, scopes, source permissions, tool actions, revocation, and suggested learning before connecting company knowledge.
3 min readAI products
Turn model capabilities into applications, voice experiences, and maintainable releases.
AI SaaS MVP architecture: build one complete workflow
Scope an AI SaaS MVP around a complete user journey, tenant boundaries, structured results, usage limits, and an evaluation-driven release decision.
3 min readVoice experiencesVoice AI agents: design the human handoff first
A practical voice-agent design guide covering task boundaries, confirmation, interruptions, failed tools, and an explicit handoff to a person.
3 min readSoftware engineering
Connect mobile, web, backend, and operational foundations.
Healthcare workflows
Define responsible operational and knowledge workflows in healthcare.
Guides connect documented project work with practical engineering decisions. Illustrative examples are labeled, and technical sources are linked in each article.
What could work
better?
Bring the business problem.
We’ll figure out the right next move.