Ammar Shah
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AI Solutions Architect Consulting · Products · Custom builds

AI that

I help businesses find where AI moves cost, speed and revenue. Then I design, build and run the systems that deliver it, so the result shows up in your numbers, not just in a demo.

Book a discovery call → Find your problem
What I do
Sound familiar?

The problems costing you more than you think.

Pick the team you run. If a line sounds like something you've said this month, tick it.

Tick the problems that sound like you, and I'll come to the call prepared.

Talk about these →
Services

Three ways to work together.

Ready-made products when the problem is common, a full build when it's yours alone, and senior advice when your own team is doing the building.

Products · fixed scope

Ready-made, fast to deploy

Proven solutions adapted to your data and tools, with a set scope and a clear before-and-after.

Company Knowledge Assistant

Answers from your own documents, with sources and permissions.

Document Automation

Extract, draft and check documents against your templates.

AI Cost Cut

Lower your AI bill with quality measured before and after.

Agent Safety Audit

We try to break your agent before someone else does.

Industry editions below ↓Browse →
Services · design, build, ship

Custom AI systems, end to end

One accountable architect from the first workshop to the day your team takes over.

AI agents & automations

Agents that do real work across your tools, safely.

Knowledge & search systems

Answers grounded in your data, traceable to source.

AI-native products

From first prototype to a live product with real users.

Rescue & harden

Make a fragile AI system ready for real traffic.

You get → a system you ownPlan a build →
Consultancy · judgment on call

Direction for your AI team

Senior architecture thinking for teams that build in-house but want it done right.

AI strategy & roadmap

Where AI pays off in your business, in priority order.

Fractional AI architect

Design reviews and technical direction, without a full-time hire.

Architecture review

An outside view of what you've built and what will break.

Build, buy or wait

Straight advice on models, vendors and cost before you commit.

You get → decisions you can defendBook a session →
Products · by industry

Ready-made for your industry.

These are the Products above, pre-built for the documents, rules and tools of your sector. Pick yours.

Don't see your industry? Every solution is built on the same foundation and adapts to new sectors quickly. Ask about yours →

Case studies

Proof, in production.

Five problems, five systems running today. Each result came from a design decision rather than a lucky prompt.

TheWINK.coAI product · WOPIE

A prototype turned into a product 1,200+ people use.

Challenge

A promising AI prototype that had to become something real people could rely on every day.

Approach

I designed the whole architecture: how the services split, how the product finds the right information and how its agents work together.

Result

A live product with 1,200+ users, built on documented decisions the team keeps extending.

Multi-agentRetrievalProduct architecture
1,200+

people on the live product.

Prototype→Architecture→Launch→1,200+ users
TheWINK.coAI running costs

The AI bill cut by 43%, and nobody noticed a difference.

Challenge

Cost and wait times were climbing with every new user.

Approach

Send each request to the smallest model that can handle it, trim what gets sent, and reuse answers to questions that keep coming back.

Result

43% lower cost and answers a third faster, with quality unchanged across the test suites.

Model routingSemantic cachingRegression testing
−43%

in inference cost.

Cost · before100
Cost · after57
Wait time · before100
Wait time · after67
HerbionRegulatory automation

Regulatory paperwork, drafted in a fraction of the time.

Challenge

Specialists spent enormous effort assembling regulatory dossiers from scattered sources and strict templates.

Approach

An AI system that gathers the evidence, drafts each section to the required template and cites where every statement came from.

Result

Preparation time down by more than 90%, with every section traceable for audit.

Agentic RAGCitationsCompliance
−90%

preparation time, every claim sourced.

By hand100
With the AI system<10
SAYNT AIMulti-agent systems

Agent teams that fail safely instead of silently.

Challenge

International clients needed agents for mission-critical work, where a quiet mistake is worse than no answer.

Approach

Agents with memory and clear rules for their tools, plus retries, circuit breakers and a path to a human. I led the developers who built them.

Result

Systems that recover or ask for help instead of guessing, and are tested before every release.

LangGraphHuman-in-the-loopTeam lead
Contain.

Every failure has somewhere safe to go.

Agent step→Retry→Circuit breaker→Human
TheWINK.coAgent security

AI agents that can't be talked into things.

Challenge

An agent that reads emails, files or web pages can be manipulated by what it reads.

Approach

Keep outside content away from instructions, check every action, give each agent only the access it needs and require a person for anything destructive.

Result

Every release gets attacked by us first, before anyone else has the chance.

Red-teamingLeast privilegeSandboxing
Every
release.

attacked in-house before it ships.

  • Outside content can't give orders
  • Every action checked before it runs
  • Agents only get the access they need
  • Destructive steps wait for a person
Why teams bring me in

Built to work on day three hundred, not just demo day.

Outcome before code

We agree on the number that has to move before anything gets built, and we measure it after.

Production, not pilots

Designed for real users, messy data and people trying to trick it, from the first day.

One accountable architect

Strategy, design and delivery stay with one person, so nothing gets lost between hand-offs.

You own all of it

Code, prompts, tests and documentation are yours. No lock-in, no black boxes.

Experience

One layer up the stack, every time.

I started with data pipelines, moved to retrieval, then agents, and now own the whole architecture. That path is why I know where systems break.

2026 — Now

TheWINK.co

AI Architect Engineer

I own the architecture behind the company's AI product and its enterprise client work. This is where cost, quality and security all became mine to answer for.

WOPIE · 1,200+ users−43% AI costSecurity & quality lead
2026

SAYNT AI

AI Architect Engineer

My first time leading a team. I moved from building agents to deciding how they should be built, and checking that they were.

Team leadMulti-agent systemsInternational clients
2025 — 2026

Herbion

Regulatory automation · Industry project

Where I learned that in a regulated industry, an AI answer without a source is worthless. Everything I build since cites its evidence.

Agentic RAG>90% time saved
2025

ITANZ

Data Engineering

I started in data. An AI system is only as good as the pipeline underneath it, and this is where that lesson stuck.

Data pipelinesBusiness dashboards
Education FAST National University (NUCES) Computer Science · 3× Dean's List
Start a project

Let's talk.

The first stepFree

AI Opportunity Call

A focused call about your business, not a sales pitch.

  • 30 minutes, one-to-one with me
  • Your top three AI opportunities, ranked by payback
  • A clear next step, whether or not we work together
ammar.shah1904@gmail.com
Open mail app

Tell me the problem, who it affects and what success would look like. I'll come back with questions and a first view of how I'd solve it.

LinkedInin/ammarshah1 ↗ GitHubammarshah451 ↗ Webammarshah.me ↗