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Forward-deployed AI engineering · UK · US

The AI problemyou can't hire for.

We're the senior engineering team that embeds with you and ships it — including the problems most AI firms decline.

From consumer products to semiconductor EDA. In production, tied to a number.

production systems
30+production systems
repeat engagement
93%repeat engagement
average delivery
1.8 moaverage delivery

30 minutes, no cost. You leave knowing where AI pays off first — and what it takes to ship it.

Deploy. Learn. Yield.

The gap

AI ambition is everywhere. Deployment is not.

The ideas already exist. So does the data. What's missing is the senior AI capability to land it inside a real business.

  • [01]

    Hiring takes months.

    And it costs heavily, then often lands you a team that's narrow in exactly the wrong direction.

  • [02]

    Tools rarely fit the stack.

    Off-the-shelf AI doesn't know your business, your constraints, or the system it has to live inside.

  • [03]

    Outsourcing executes tickets.

    Capacity is easy to buy. Owning the hard AI problem is not what a ticket queue does.

  • [04]

    Consulting stops at strategy.

    The deck arrives on time. The working system doesn't arrive at all.

The answer

Bring in the team that ships it.

We don't sell you a tool and wish you luck. We put senior AI engineers inside your team and own the path to production.

Your business context

  • Data & systems
  • Applications
  • Team & domain expertise
  • Goals & constraints

DLY AI FDE team

  • Senior AI engineers
  • Full-stack capability
  • Product & domain savvy
  • Embedded & accountable

Production system

  • Shipped
  • Reliable
  • Measurable

Live learning loop

  • Deploy
  • Observe
  • Learn
  • Improve

Senior AI engineers. Embedded.

Remote by default. On-site when needed.

Inside your stack. Owning the outcome.

Discovery → deployment → iteration.

Give every company a world-class AI engineering team.

The alternatives

Faster than in-house. Deeper than outsourcing.

Four ways to get AI built. Only one of them is senior, embedded and end-to-end.

How DLY's forward-deployed model compares with building in-house, traditional outsourcing and big consulting, scored out of 4.
In-housecontrol, but slow and narrow.Outsourcingcapacity, but ticket-led.Big consultingaccess, but strategy-heavy.DLY FDEsenior, embedded, end-to-end.
SpeedIn-house: 1 out of 4Outsourcing: 2 out of 4Big consulting: 1 out of 4DLY FDE: 4 out of 4
AI depthIn-house: 2 out of 4Outsourcing: 1 out of 4Big consulting: 3 out of 4DLY FDE: 4 out of 4
Embedded contextIn-house: 3 out of 4Outsourcing: 1 out of 4Big consulting: 2 out of 4DLY FDE: 4 out of 4
Production ownershipIn-house: 3 out of 4Outsourcing: 1 out of 4Big consulting: 1 out of 4DLY FDE: 4 out of 4

DLY FDE is a distinct category. Senior. Embedded. End-to-end.

How we start

Start with the deployment path.

The first step costs you nothing and ends with a straight answer: where AI can be deployed in your business, and what to do first.

  1. 01

    Free AI assessment

    Where AI can be deployed, and what's worth shipping first.

    Free → paid

  2. 02

    Paid discovery + solution design

    The problem, the architecture, the delivery plan and the commercial scope.

  3. 03

    Build + deploy

    Design, engineer, test and ship the working system.

  4. 04

    Embedded iteration

    Learn from real use. Improve the system. Extend the value.

  5. 05

    Handover or keep scaling

    Documentation, training and continued support — as much or as little as you need.

Paid work begins with technical discovery. The boundary is explicit, always.

Track record

Real systems. Across hard domains.

domains
20+domains[01]
production systems
30+production systems[02]
repeat engagement
93%repeat engagement[03]
average delivery cycle
1.8 moaverage delivery cycle[04]

100+senior engineers · expert network

H1 2026: 10+ mid-to-large engagements

Delivery span

  1. Consumer products
  2. Fintech
  3. Healthcare
  4. Industrial
  5. Enterprise SaaS
  6. Space systems
  7. Semiconductor EDA & scientific systems
What we shipped

The hard part, shipped.

Every engagement targets a working system tied to a number. Client identities stay anonymous; the outcomes are real.

[01]Enterprise research

An enterprise research workflow, rebuilt around AI.

An enterprise market-intelligence organisation

The hard part

Sources, analysis and reporting lived in three disconnected places. Every study restarted the work from zero.

What we shipped

One AI research workspace joining data sources, analysis tasks, knowledge assets and structured report generation — plus management visibility across all of it.

  1. Data
  2. Analysis
  3. Reports

What it moved

No unverified performance metric shown.

Client anonymised

Enterprise AI research analysis workspace with report generation
[02]Legal tech

From legal search to booked counsel.

A legal-tech platform

The hard part

Users didn't know which lawyer they needed. Only 1 in 5 visitors ever completed a booking.

What we shipped

An AI intake, lawyer-ranking and appointment-booking workflow that turns a vague problem into the right specialist.

What it moved

booking conversion
3.1×booking conversion
to first booking
<4 hrsto first booking
star average · 500+ reviews
4.9star average · 500+ reviews

Client anonymised

AI legal consultation booking product on mobile
[03]Travel tech

From 12-minute activation to 90 seconds.

A global travel-tech company

The hard part

Plan confusion, manual APN setup and invisible overseas usage created friction — and a support queue.

What we shipped

Itinerary-based eSIM recommendation, automated activation and live usage management.

What it moved

activation, from 12 min
90 secactivation, from 12 min
support tickets
−76%support tickets
repeat purchase
58%repeat purchase

Client anonymised

eSIM purchase and management product on mobile
[04]Engineering manufacturer

134 minutes to 7.

A specialised engineering manufacturer

The hard part

Drawing correction, process routing and SAP hand-offs all depended on repetitive work and senior-engineer knowledge that lived in people's heads.

What we shipped

Agent-executable engineering workflows connecting drawing recognition, geometry and tolerance validation, process-route optimisation and the systems of record.

What it moved

per drawing, from 134
7 minper drawing, from 134
accuracy
96.5%+accuracy
rework
−89%rework
manual steps eliminated
150+manual steps eliminated

Client anonymised

  1. Engineering drawings

    • PDF
    • DWG
    • TIFF
    • STEP

  2. Drawing recognition

    • OCR / CAD parse
    • Feature extraction

  3. Agent orchestration

    • Geometry & tolerance validation
    • Correction & normalisation
    • Process routing optimisation
    • BOM & operation mapping
    • Quality & manufacturability checks

  4. Optimised output

    • DWG
    • PDF
    • STEP

  5. Systems

    • BOM
    • Route
    • Operations

Also shipped

  • AI health companion app

    AI health companion

    64% 7-day retention

    1.9× daily check-ins · zero missed safety escalations in a 90-day audit

  • Online AI fitness coach app

    AI fitness coach

    72% workout completion

    form-related injury reports down 58% · 30-day renewal up 22%

  • AI social matching app

    AI matching

    2.7× match-to-conversation

    first-week churn down 34% · first message under 2 minutes

  • Privacy-first people map app

    Privacy-first people map

    3× in-person meetups

    zero location-privacy complaints · 30-day retention up 19 points

  • AI personal shopper commerce app

    AI personal shopper

    +38% conversion

    size-related returns down 27% · average order value up 15%

  • AI crypto wallet app

    AI wallet safety layer

    −91% mis-sent transactions

    swap volume up 2.4× · onboarding under 3 minutes

Client anonymised · white-label engagements

Underneath

The stack underneath the work.

  1. 06

    AI applications

    • consumer
    • enterprise
    • research
    • automation
  2. 05

    Agent platform

    • agents
    • RAG
    • workflow
    • memory
    • tool calling
  3. 04

    Model infrastructure

    • LLM gateway
    • routing
    • controls
  4. 03

    Data + knowledge

    • vectors
    • search
    • knowledge bases
    • pipelines
  5. 02

    Distributed services

    • Go
    • Java
    • Python
    • TypeScript
    • microservices
  6. 01

    Cloud infrastructure

    • Kubernetes
    • private cloud
    • on-prem

Representative delivery stack, adapted to the client environment.

Who you get

We start where the problem gets hard.

The hard part cannot be hand-waved.

I came to AI from physics — the world of problems you can't hand-wave. What I found in the field was a gap: enormous efficiency and R&D problems sitting undone, because the people who can solve the hard part and the people who understand the business are almost never the same. So we stopped selling tools and started bringing the team.

Physics. Engineering. Software. AI. UX.

From the founder of DLY AI.

Next step

Find the first AI deployment worth shipping.

  • Book a 30-minute technical consultation.
  • Get a free assessment of where AI can be deployed.
  • Leave knowing what should happen first.
  • Paid discovery and solution design comes next.
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