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Applied AI engineering studio

Tell us what your business needs.
We build the AI that does it.wins the lead.kills the backlog.answers in seconds.catches the error.

No product to buy. No fixed menu. We start from an outcome you already care about — more of the demand you're losing, faster answers for customers, fewer hours lost to manual work — and build the system that moves that number.

Start a project See the outcomes
You own the code No vendor lock-in Built on your cloud
The whole idea

Nobody wants an AI system. They want the result it produces.

No one has ever needed retrieval-augmented generation. They needed their sales team to stop losing enquiries over the weekend. They needed a two-week approval cycle to take two days. They needed to open a second branch without doubling the back office.

So the first conversation is about your business, not our stack. What's slow, what's expensive, what breaks when volume goes up, what your team keeps apologising to customers for. Then we work backwards to whatever technology is genuinely required.

AI is a great answer when…

The work is high volume, pattern-shaped, and currently done by people reading, sorting, drafting or checking things. That's most of what slows a growing business down.

…and a bad one when

The real problem is unclear ownership, data nobody maintains, or a process that changes every quarter. We'll tell you which one you've got, and we'll tell you for free.

Six outcomes

What businesses actually hire us to change

Every engagement lands in one of these, whatever the industry. If yours isn't here, it's still worth a conversation — this is where we've been, not where we can go.

You'll recognise this if

  • ▸Leads that come in Friday evening get answered Monday afternoon
  • ▸Your best salesperson spends half the week qualifying people who were never going to buy
  • ▸Quotes take days because someone has to dig up the last similar one
  • ▸Follow-up happens when someone remembers, not when it should

What we build

  • →Instant first response that reads the enquiry and answers the obvious questions
  • →Qualification against your real criteria, enriched from public sources
  • →Routing to the right person with a summary, not a raw forward
  • →Proposal and quote drafting from your own past documents and pricing rules
  • →Follow-up that stops the moment a human replies

What changes

  • Time to first responseHours → minutes
  • Enquiries handled per repUp, no hiring
  • Time on unqualified leadsDown sharply
  • Quote turnaroundDays → same day

Usually starts with the inbox or form your demand arrives through, and the CRM it should end up in.

You'll recognise this if

  • ▸The same forty questions make up most of your ticket volume
  • ▸Answer quality depends on which agent picks it up
  • ▸Support headcount rises in a straight line with customer count
  • ▸Your team rewrites the same explanation several times a week

What we build

  • →An assistant grounded only in your approved material, with citations
  • →Drafted replies your agents edit and send rather than write from scratch
  • →Confident self-service for questions that genuinely have one answer
  • →Clean escalation the moment the system is unsure or the customer is unhappy
  • →Gap reporting showing which questions your docs can't answer

What changes

  • First response timeDown by most
  • Repeat questions reaching a humanSharply cut
  • Answer consistencySame across team
  • Cost per resolutionFalls as volume rises

Usually starts with a year of past tickets and whatever documentation already exists.

You'll recognise this if

  • ▸People re-key information from one system into another every day
  • ▸Invoices, forms or records arrive as PDFs and scans someone must open
  • ▸Month-end means a week of reconciliation and chasing
  • ▸Approvals sit waiting because the approver lacks the full picture

What we build

  • →Intake that classifies documents and pulls out the fields that matter
  • →Validation against your own rules, reference data and prior records
  • →A review queue for low-confidence items only, not everything
  • →Automatic routing, filing and system updates once a record clears
  • →An audit trail covering input, decision, model version and reviewer

What changes

  • Hours per week on manual entryMost returned
  • Documents needing a humanTypically under 15%
  • Throughput at same headcountSeveral times higher
  • Error and rework rateMeasured, then cut

Usually starts with the single document type that eats the most hours.

You'll recognise this if

  • ▸A simple question takes two days because it needs three people
  • ▸Critical knowledge lives with one person who's about to take leave
  • ▸New joiners take months to become useful
  • ▸You find out about problems from a customer, not a report

What we build

  • →A grounded internal assistant over your drives, wikis, tickets and databases
  • →Permissions honoured at retrieval, so people see only what they should
  • →Plain-language querying over operational data, with the figures shown
  • →Scheduled summaries and anomaly flags on the numbers you watch
  • →Onboarding paths built from the questions new joiners actually ask

What changes

  • Time to answer an internal questionDays → seconds
  • Dependence on one key personReduced
  • Time to productivity for new hiresMuch shorter
  • Decisions made on current dataMost of them

Usually starts with the five questions your team asks each other most often.

You'll recognise this if

  • ▸Review is a bottleneck because only two people can do it properly
  • ▸Mistakes get caught by customers rather than by a check
  • ▸Audit prep means weeks of assembling evidence after the fact
  • ▸Contracts get signed without anyone comparing them to the last version

What we build

  • →Automated first-pass review against your checklists and prior examples
  • →Deviation flagging that shows what changed and why it matters
  • →Expert queues ordered by risk rather than arrival time
  • →Evidence captured as work happens, so audit prep is a query
  • →Sampling and monitoring that proves the check still works

What changes

  • Coverage of reviewSample → everything
  • Expert time per itemSpent where needed
  • Issues caught before releaseSubstantially more
  • Audit preparationWeeks → hours

Usually starts with the check that currently gets skipped when things are busy.

You'll recognise this if

  • ▸Customers are asking whether your product does this yet
  • ▸A competitor shipped something and your roadmap has no answer
  • ▸You hold data that'd be far more valuable interpreted than stored
  • ▸There's an obvious premium tier you can't staff your way into

What we build

  • →Customer-facing AI features designed as product, not a bolt-on chat box
  • →Per-customer data isolation and permissions built in from the start
  • →Unit economics modelled before launch so the pricing actually works
  • →Quality guardrails, because this failure mode is visible to your customers
  • →Usage analytics showing which part of the feature earns its keep

What changes

  • New revenue lineA tier you can price
  • Competitive positionAnswer on the table
  • Cost per active userModelled pre-launch
  • Retention on the featureMeasured day one

Usually starts with the feature request you hear most on sales calls.

Sound familiar?

If any of this is your week, there's something here

Real sentences from first calls. Every one of them became a system.

SALES"We lose deals to whoever replied first, not whoever was better."

FINANCE"Three people spend month-end matching invoices to purchase orders."

SUPPORT"Our answers depend entirely on which agent picks up the ticket."

OPERATIONS"Everything stops when one person is on leave."

LEGAL"Contracts get signed without anyone comparing them to the last one."

HR"New hires ask the same twenty questions for their first two months."

PROCUREMENT"We can't tell what we're actually spending across suppliers."

LEADERSHIP"A simple question takes two days and three people to answer."

PRODUCT"Customers keep asking if we do this yet, and we don't."

ManufacturingLogisticsFinancial servicesHealthcare adminProfessional servicesRetail & D2CReal estateInsuranceEducationSaaS

Industry matters less than shape. A law firm reviewing contracts and a logistics company checking delivery paperwork are the same engineering problem wearing different clothes. We look for volume, a pattern, and a cost you can already point at.

Under the hood

The outcome is the point. This is what makes it hold up.

Whatever the business goal, every system ships with the same foundations — because these are what separate something that works in a meeting from something that works in March.

Evaluation before deployment

A labelled test set from your real cases, scored on every change. Nothing reaches production on the strength of a good demo.

  • Golden datasets
  • Regression suites
  • Human review loops

Observability from day one

Every call traced with inputs, outputs, latency, cost and model version. When behaviour shifts you hear it from a dashboard, not a complaint.

  • Request tracing
  • Cost per workflow
  • Drift alerts

Guardrails and failure modes

We design what happens when the system is unsure, when a tool fails, and when someone tries to misuse it. The escalation path is part of the build.

  • Confidence thresholds
  • Scoped permissions
  • Human escalation

Model-agnostic architecture

The provider sits behind an interface. When a better or cheaper model lands, you change a config rather than a codebase.

  • Provider abstraction
  • Prompt versioning
  • Fallback routing

Your infrastructure, your data

Deployed into your cloud accounts with your keys and your retention rules. We work inside your VPC where policy requires it.

  • Your cloud
  • VPC deployment
  • No training on your data

Handover as a deliverable

Decision records, runbooks and a walkthrough with your engineers. The measure of a good engagement is that you could fire us and keep running.

  • ADRs
  • Runbooks
  • Source and prompts
The work

Clients stay private. Patterns don't.

These systems sit close to how a company operates, so we don't publish names, data or results.

Revenue

An enquiry that answers itself, up to a point

Leads read, enriched and answered with real context, then handed to a person at the exact moment a person adds something.

Lead triageCRM syncEscalation rules
Public reference ↗
Operations

Documents in, decisions moving

Validate, extract and route the files sitting in a queue, with every decision traceable to the page it came from.

ExtractionValidationAudit trail
Public reference ↗
Service quality

Approved answers, without the queue

Frontline teams get accurate, pre-approved responses and the right attachments instead of rebuilding the same answer every week.

Knowledge baseApprovalsResponse drafting
Public reference ↗
Finance & review

Review built around judgement

Documents, checklists and prior examples arrive together, so the expert spends attention only on the part that needs an expert.

ComparisonControlsReviewer queue
Public reference ↗

Anonymised system patterns, not claims of past client work. Linked references are public case studies published by other organisations, included because they informed how we map opportunities.

How it runs

Four stages, each with an exit condition

You can stop after any of them. Nothing is designed to trap you in the next phase.

01

Map & scope

We sit with the people doing the work, trace how it actually moves, and find where the hours and errors concentrate. You keep the map either way.

1–2 weeks
Exit conditionA scoped plan you can act on

02

Design & prove

Architecture, data flow, failure handling and the evaluation set. We prove the hardest assumption on your real data before committing to a build.

1–3 weeks
Exit conditionA tested approach, a measured baseline

03

Build & integrate

Weekly working software into a staging environment your team can use. Integration happens throughout, not at the end.

4–12 weeks
Exit conditionIn production with real users

04

Run & extend

We watch the metrics that matter, fix what the first version got wrong, and expand only where results have earned it. Or we hand over and leave.

Ongoing
Exit conditionYour team running it without us
Ways in

Three ways to work together

Start wherever you are. Most businesses move through these in order, and every scope is quoted after we've mapped the work.

Start here

Discovery sprint

Two weeks to a straight answer on which outcome is worth chasing first, and what it would take to get there.

  • →Workflow mapping with your team
  • →Opportunities ranked by effort and impact
  • →Technical architecture for the first build
  • →A scoped proposal, no obligation
Book a sprint
Most common

System build

One team carrying a defined outcome from design through to daily use by your people.

  • →Everything in the discovery sprint
  • →Full design, build and integration
  • →Evaluation suite and observability
  • →Rollout, training and documentation
  • →30 days of post-launch support
Plan a build
Long game

Embedded team

Senior AI engineering inside your team, for companies building this as a permanent capability.

  • →Dedicated senior engineers
  • →Architecture and code review
  • →Internal enablement for your team
  • →Documented exit plan from month one
Discuss a team
Our rules

How we work, and what we won't do

We'll tell you not to build it

A real share of scoping calls end with us saying the problem is a process problem, a database query, or a hiring decision. That answer is free, and for some clients it's the most valuable thing they get from us.

No proof-of-concept theatre

We don't build demos designed to impress a board. If it isn't going into real use with real users, it isn't worth either of our time.

You own everything

Source code, prompts, evaluation sets, infrastructure definitions. Deployed on your accounts from day one. There's no Aetrax platform you become dependent on.

The outcome gets a number

Before we build, we agree what should move and how it'll be measured. A system that can't be shown to have changed something is a system nobody will defend at budget time.

We name the failure modes first

Before a build starts you get a written list of how the system can be wrong, what happens when it is, and who gets told. Surprises in production are a design failure.

Your data is not our training data

We work inside your infrastructure where policy requires it, and we never use client material to train or improve anything outside that engagement.

Straight answers

Before you get in touch

That's the normal starting point and exactly what a discovery sprint is for. You don't need a technical brief. Bring the frustration — the process that always slips, the team that's always behind, the customers who always complain about the same thing — and we'll trace it back to something buildable, or tell you it isn't.

Systems are deployed into your cloud accounts using your keys and your retention rules. Where policy requires it we work inside your VPC and against private model endpoints, so your content never leaves your boundary. We sign an NDA before the first technical conversation, and client material is never used to train or improve anything beyond that engagement.

It will, at some rate, and the design work is deciding which rate is acceptable and what happens at the edges. Before the build starts you get a written list of failure modes, a confidence threshold below which work goes to a person, and an escalation path. The evaluation suite measures the error rate continuously rather than once at launch.

You do, in full, from the first commit. That includes source code, prompts, evaluation datasets and infrastructure definitions, in your repositories and your cloud accounts. We retain no licence and there's no Aetrax runtime you depend on.

No, and waiting until it's clean is how these projects never start. Assessing the state of your data is part of scoping, and a fair amount of the build is usually handling mess gracefully. What we do need is someone who owns the source material — if nobody's responsible for keeping it current, no system built on top of it will stay right.

Yes, and it usually makes the result better. We work in your repositories, your review process and your ticketing system. On embedded engagements that integration is the point, with decision records and enablement sessions so your team can extend what we build.

Because these systems sit close to how a company operates, and most clients would rather competitors didn't know what they've automated. We share references privately with permission, and we'd extend you the same treatment. Logos on a website are the cheapest form of proof anyway.

Every build includes 30 days of support. After that you can take it fully in-house with the runbooks and handover sessions, or keep us on a lighter retainer for monitoring, model updates and extensions. We're explicitly designed to be removable.

The crew

The people who do the work are the people you meet

A two-person core that does the work itself. Every engagement has a named lead who stays with it from scoping to handover — no account manager between you and the build.

Mohamed Munavar Hussain Founder · Gen AI Engineering Mohamed Munavar Hussain Two years building agentic and generative AI in production — multi-agent systems on AWS Bedrock, document extraction pipelines, and RAG over enterprise knowledge. Leads scoping and architecture, and stays on every engagement from the first call to handover. LangChainLangGraphAWS BedrockMulti-agentRAGPython View portfolio ↗ Mohamed Arshath Technical Lead · Applications Mohamed Arshath Three years shipping enterprise cross-platform applications used by thousands of people daily, and leading a modernization programme from .NET MAUI to Flutter. Builds the interfaces and integrations that turn a working system into one people actually open. Flutter.NET MAUIC#REST & SOAPOracleAzure View portfolio ↗

Specialists are brought in per brief — security, data engineering and domain review — when the work calls for it.

Let's go

You don't need to know what to build.

Describe what's slow, expensive or breaking. We'll come back within two working days with a view on whether it's worth building, and what the first step looks like.

munavarhussain@outlook.com

Used only to reply to your enquiry. We won't add you to a mailing list.