Strength 02 · AI Product Engineering

AI-Native Product Engineering.

Build AI-first SaaS, copilots, RAG systems, browser and WhatsApp agents, AI dashboards, internal tools, and AI-enabled mobile and web apps - architected for production load, not for a demo video.

80+
Production apps shipped
15+
Industries served
0
Junior hand-offs
100%
Code ownership yours
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The Build Order

How AI-First Products Get Built.

An AI product is not a wrapper around a model. It is retrieval, state, evaluation, cost control and failure handling - designed before the first feature ships, because retrofitting them after launch is where budgets die.

01

Scope & Model Strategy

What genuinely needs a model, what needs plain code, and what should not be built yet. The cheapest feature is the one you talked yourself out of.

02

Retrieval & Data Architecture

Ingestion, chunking, indexing, permissions and freshness designed around your real corpus rather than a tutorial dataset.

03

Product Build

SaaS, copilot, dashboard, browser or WhatsApp agent, mobile and web surfaces - built against live data from the first sprint.

04

Evals, Cost & Launch

Automated eval suites, token ceilings, latency budgets, monitoring and a rollout you can reverse.

The Shift

From Impressive Demo To Durable Product.

The gap between a prototype that wins the room and a product that survives a Monday is almost always one of these four things.

Where teams are today
Where this puts them
The prototype impressed everyone and then fell over on real data volume.
Architecture decided before the build: retrieval, state, permissions, fallbacks.
Model costs scale faster than revenue because nobody set token ceilings.
Token ceilings, caching and model routing that keep unit economics survivable.
No evals, so every prompt change is a gamble nobody can measure.
Automated eval suites so changes are measured, not guessed at.
Retrieval returns confident nonsense because chunking was an afterthought.
Permission-aware, freshness-aware retrieval designed around your actual corpus.

What You Get

Six Surfaces, One Hardened Backend.

Whatever the front end is, it runs on the same production architecture underneath.

01

AI-First SaaS

Multi-tenant AI products with the billing, permissions, audit and admin surfaces a real SaaS needs.

Multi-tenant Billing Admin
02

Copilots & Assistants

In-product copilots grounded in your data, with tool access, citations and safe failure behaviour.

Tool use Citations Guardrails
03

RAG & Retrieval Systems

Enterprise retrieval over documents, tickets, contracts and databases with permission-aware indexing.

Hybrid search Permissions Freshness
04

Browser & WhatsApp Agents

Agents that operate where the work already happens - the browser, WhatsApp, email, and internal portals.

WhatsApp Browser Email
05

AI Dashboards & Internal Tools

Operational dashboards and internal tooling that read live systems and explain what changed and why.

Live data Ops tooling Explainability
06

Mobile & Web Apps

AI-enabled mobile and web applications built on the same hardened backend architecture.

iOS / Android Web Shared backend
Shipped · From The Field

A B2B SaaS team needed a copilot over eight years of client documents and a live operational database. I designed the retrieval layer, built the copilot and an AI dashboard, and put an eval suite around the answers before anything reached a customer.

FastAPI Backend Hybrid Retrieval pgvector Automated Evals
<200ms
Retrieval latency
94%
Eval pass rate
11wk
To launch
3×
Support deflection

Engagement

Architecture First, Features Second.

Weekly milestone demos throughout, with real code in staging you can test from the first build sprint onward.

Weeks 1-2

Architecture

Scope, model strategy, retrieval design and cost modelling before any feature work.

Weeks 3-6

Core Build

Retrieval layer, agent logic and primary product surface built against real data.

Weeks 7-9

Harden

Eval suites, load testing, token ceilings, monitoring and failure routing.

Weeks 10+

Launch

Rollout, documentation and knowledge transfer to your engineering team.

Straight Answers

Questions Founders Ask First.

Can you work on an existing product instead of starting fresh?

Yes, and that is the more common engagement. I find the seams where AI fits into a live product without a twelve-month rebuild, then build against those seams.

Which models and frameworks do you use?

Whichever the workload justifies. Model choice is a cost, latency and accuracy decision made per feature, and the architecture keeps routing swappable so you are never locked to one provider.

Who owns the code?

You do, entirely. Full repository ownership, architectural documentation and direct knowledge transfer to your team. No agency lock-in and no hosted black box.

How do you control AI running costs?

Token ceilings per workflow, caching, model routing by task difficulty, and cost monitoring wired in from day one rather than discovered on the third invoice.

Keep Looking

The Other Three Strengths.

01 AI Workflow Automation / Agentic Ops Business process automation and process compression across nine business functions. View 03 Vibe-Code / AI-Built MVP Launch Review Audit, repair, secure, test and productionize AI-generated codebases. View 04 AI Enablement Retainers Recurring adoption, process redesign, training, governance, evals and improvement. View

Have an AI Product to Build or Rescue?

Talk directly with a systems architect who has shipped 80+ production applications across 15+ industries. No account managers, no discovery theatre - just an honest read on what to build, what to skip, and what it will actually cost to run.

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Typical response time: under 24 hours · Founder-to-founder, no account managers