Your AI pilot works in the demo. We take it into production.

With numbers your stakeholders can sign off

We measure where your pilot really stands, fix what holds it back, and take it live in your cloud or on-prem.

Book a pilot evaluation See our work
Pilot scorecardEXAMPLE
MeasureBeforeAfter
Answer quality71%93%
Resolved end to end48%84%
Cost per task$0.19$0.04
Response time14 s5 s
✓ All targets met · signed off
Illustrative example, not a client result
Photo: Unsplash

Our team has delivered AI and data systems for

ANTHROPICElsevierDRDOL&TP&GApex CoVantageFlexiVan

Work delivered by members of our team, including through our delivery partner. Names are shown for identification only and do not imply endorsement. Disclaimer

Results that reached production

For teams that run systems they cannot switch off, on data they cannot move.

Aerial view of a large solar farm
15 plants

One live operations platform for solar power

Engineering & power company
 Server racks and network cabling
0 data egress

Air-gapped ML that passed a government audit

Defence research laboratory
 An aisle of server cabinets
Weeks → hours

Agents that plan cloud migrations

AWS and Azure migration teams

Why most AI pilots never reach production

It is rarely the model. It is the absence of proof that it is accurate, affordable and safe.

  • No evaluation on real cases
  • No cost ceiling at production volume
  • Security review comes last
  • Data approved for the pilot only
  • No owner for wrong answers
  • Runs beside the process, not in it
Why each one mattersHide
No evaluation on real cases
Quality is judged by demos and opinions, so nobody can prove it is good enough.
No cost ceiling
The demo was cheap. Nobody has priced it at production volume.
Security review comes last
The review starts after the build and sends it back.
Data approved for the pilot only
Access to production systems was never agreed.
No owner for wrong answers
Nobody is accountable for errors once it ships.
Runs beside the process
Without integration, people have to remember to use it.

What we do

Consulting and engineering from one team. You own everything we build.

Start here

Pilot evaluation

Where your pilot stands, and why it is stuck

1–2 weeks

A baseline measured on your real data, the root causes in plain language, a stakeholder presentation with a go / no-go, and a step-by-step production plan.

Discuss an evaluation
Engineering

Unstick & ship

Fix the root causes and go live

4–6 weeks

We fix evaluation, guardrails, cost and integration, report a weekly scorecard, and go live only when the agreed targets pass.

Discuss a build
Engineering

Private & on-prem AI

AI that runs inside your walls

6–10 weeks

LLM and ML platforms inside your environment, air-gapped where required, with access control and audit logging built to pass your security review.

Discuss private AI
Engineering

Data platform modernisation

Faster, cheaper data pipelines

Scoped

Assessment, target architecture, cost model and migration on Spark, Databricks, Snowflake and Airflow, across AWS and Azure.

Discuss your platform
Engineering

Industrial & energy data

From sensor data to operator alerts

Scoped

SCADA and sensor pipelines at scale, failure prediction and live operations dashboards for plant and fleet operators.

Discuss your plants

Built on the platforms you already run

AWSGoogle CloudDatabricksSnowflakeSparkKafkaAirflowKubernetesPyTorchHugging FaceOpenAIAnthropicLangChainPostgreSQL

An outcome at every milestone

Each step ends with numbers you can check. Then you decide whether to continue.

01

Baseline

Weeks 1–2

Signed-off targets and an ROI case

DetailsHide

We measure your pilot on your real cases, inside your environment, and agree targets for quality, cost, speed, safety and business value with business, security and finance.

You decide: go, fix first, or stop.

02

Prove

Weeks 3–4

Measured gains on the same cases

DetailsHide

We fix the biggest root causes first and re-run the same evaluation, so every gain is measured against the baseline. The ROI estimate is updated.

You decide: release to real users?

03

Go live

Weeks 5–8

Live usage and business numbers

DetailsHide

A staged release with monitoring, guardrails and human review where the risk needs it. It ships only when the agreed targets pass, and the numbers go on a dashboard your team can open.

You decide: widen the rollout.

04

Optimise

Monthly

Realised ROI, and what to improve next

DetailsHide

We review the numbers with you, tune cost and quality, and hand over runbooks so your team can run it without us.

You decide: where to invest next.

We measure the return, not just the release

Three layers, from week one. Every number is shared with you.

01

Business value

Is it paying back?

What we trackHide
  • Hours saved and cost per case
  • Cases resolved without a person
  • Revenue or conversion effects
  • Errors and rework avoided
02

Quality & adoption

Does it work, and do people use it?

What we trackHide
  • Accuracy on your real cases
  • Tasks completed end to end
  • Escalation and override rates
  • Active users and repeat use
03

System health

Can it run at full volume?

What we trackHide
  • Cost per task at real volume
  • Response time (p95)
  • Uptime and failed runs
  • Guardrail and data-access checks
Monthly ROIEXAMPLE
Net value per month$32,200Invoice-processing workflow · 64% handled without a person
See the workingsHide the workings
Invoices processed18,000
Handled without a person64%
Staff hours saved1,150 h
Value of time saved$34,500
AI running cost−$2,300
Net monthly value$32,200

Every input comes from logged data and is re-measured monthly. Illustrative example, not a client result.

Case studies

Client names are withheld. On a call we walk you through the architecture and trade-offs.

Defence R&D · Air-gapped

No-code ML for materials scientists, on an air-gapped GPU cluster

0data leaves the lab
Passedgovernment security audit
Read the case studyHide

Government defence research laboratory · Team of 4–5 · 4–5 months

Challenge

Scientists had valuable experimental data (tables, microscope images, chemical structures) but no ML engineers. The cluster had no internet access, and the platform had to pass a government security review.

What we built

  • Click-to-train ML with model comparison
  • Microscopy image analysis: classification, segmentation, similarity
  • Chemistry features from molecular structures
  • An offline bundle with one hardened entry point and separate CPU and GPU queues
Deployment architecture of the air-gapped ML platform
Deployment architecture, anonymised. Click to enlarge.
Energy · Industrial IoT

An operations data platform for 15 solar power plants

15plants on one platform
~30 GBprocessed every day
Read the case studyHide

Large engineering and power company · India & Middle East · Team of 10 · 8 months

Challenge

Each plant produced SCADA, inverter and weather data in a different format, often behind restrictive networks. Failures were found only after generation was lost.

What we built

  • An on-site agent at each plant shipping data securely to the cloud
  • Spark pipelines normalising about 2 GB per plant per day
  • Failure prediction on inverter and string data, surfaced as alerts
  • Near-real-time dashboards with single sign-on
Rows of solar panels at a utility-scale plant
Agentic AI · Cloud migration

Agents that read legacy codebases and plan their move to the cloud

Weeks → hoursper assessment
−50%model cost, same quality
Read the case studyHide

In production with AWS and Azure migration teams · codebases of 2M+ lines

Challenge

Teams spent weeks reading legacy applications by hand. Only a reliable, low-cost system would hold up at this scale.

What we built

  • Multi-agent workflows mapping dependencies, data and business logic
  • Generated target architectures, business cases and roadmaps
  • A database-migration module for schema and code impact
  • Model routing: a large model for reasoning, a small one for volume
An aisle of server cabinets
Logistics · Computer vision

Chassis and container IDs read automatically from yard video

Video → recordno manual logging
Every unitregistered and tracked by ID
Read the case studyHide

Intermodal chassis provider, US

Challenge

Chassis numbers and container IDs had to be captured from yard camera footage with varying quality, angles and lighting.

What we built

  • Automated frame extraction from recorded video
  • Image enhancement for difficult lighting and angles
  • Object detection to locate the ID regions
  • OCR to read and validate each ID into the right record
Shipping containers with printed identification codes

More projects our team has delivered

Delivered by our team members, including through our delivery partner. Details are summarised and client identities withheld where required.

AI platformFaster at scale

Claude API wrapper for parallel workloads

Built with the Anthropic team to speed up parallel processing for client workloads.

Healthcare publishingHIPAA-compliant

RAG chatbot over Elsevier data

A retrieval pipeline and chatbot over medical content, built to HIPAA data-compliance requirements.

Public sector · US65,000 documents

Semantic search for federal contracts

Hybrid keyword and semantic search over contract listings and attachments.

Financial data70% faster

Q&A over SEC 10-K and 10-Q filings

A fine-tuned open-source LLM answering analyst questions with sources.

Compliance · PublishingAuto-tagged

XBRL tagging for SEC filings

NLP models generating XBRL tags for 10-K and 10-Q filings, replacing manual labelling.

Oil & gas200 fields / document

Key-value extraction from industry documents

OCR with pre- and post-processing for engineering documents.

Automation softwareAny format

Multimodal LLM document extractor

PDFs, images, Word and Excel extracted through a chat UI and APIs for an RPA product.

Media rightsMillions of images

Reverse image search for copyright

Image similarity that automates infringement detection.

Retail · Data60% faster

Spark to Databricks migration

A large FMCG pipeline at half the cost.

Show more projectsShow fewer
Supply chain · Finance3-way match

Fraud analytics on SAP data

Purchase order, invoice and goods receipt matched to flag suspicious spend.

Consumer goodsFewer buses

Employee transport route optimisation

Routing and clustering with a savings dashboard for a Fortune 500 manufacturer.

InfrastructureRoad assets

Road asset detection from vehicle cameras

Semantic segmentation of sidewalks, guardrails and lane markings.

PublishingLLM triage

Content classification for editing

Light, medium or heavy edit needs, with an error-density score.

Energy · UKPredictive

EV charging station analytics

Descriptive and predictive analytics for charging networks.

FacilitiesDaily reports

Cafeteria consumption tracking

Computer vision counting plates and food use from CCTV.

Software analyticsReal time

Product quality analytics platform

Live insight into feature usage, behaviour and software quality.

Lekhaj Reddy, founder of Pilot2Live

Lekhaj Reddy

Founder & Principal Consultant

I lead every evaluation personally. If your pilot should not go live, I will tell you, and show you the numbers.

IIT KharagpurB.Tech + M.Tech
15 yearsEngineering leadership
30+ engineersAI, data, backend, cloud
More about meHide

Fifteen years building and leading engineering teams, most recently taking AI and data platforms into production for defence, energy and enterprise clients, and leading an organisation of 25–30 engineers across parallel projects.

View LinkedIn profile

Our commitments

Agreed scope up front

Scope, timeline and success measures in writing.

An honest no

If it should not be built, we say so.

Your data stays yours

We work inside your accounts. Nothing is copied out.

Paperwork first

NDA, GDPR terms and your security questionnaire.

You own the IP

All code, models and documents are yours.

One accountable lead

A named lead and a weekly written status.

For consultancies with more AI work than people

White-label delivery capacity. You keep the client; we deliver under your brand.

Talk about capacity
  • Teams of 2–5 engineers plus an architect
  • Staffed in about two weeks
  • First sprint at a reduced rate
  • Overlap with US East mornings and the UK day

Questions

What exactly do we get from the evaluation?

A scorecard measured on your real data, the root causes in plain language, a stakeholder presentation with a go / no-go, and a step-by-step production plan.

How do you measure ROI?

In week one we agree the business measure the work should move, such as hours saved, cases resolved or errors avoided, and record today's baseline from your data. Running costs are logged per task. Every month we compare realised value with the original case and share the workings, so finance can check them.

Does our data leave our environment?

No. We work in your cloud accounts or on-prem systems under your access controls.

Who owns the code and IP?

You do. All work product is assigned to you under the contract.

Can our own engineers work with you?

We prefer it. Pairing is how the handover works, so you are not dependent on us afterwards.

Where is the team, and what about time zones?

Our engineering team is based in India, with daily overlap with US East mornings and the full UK working day. You get one named lead and a weekly written status.

Do you sign NDAs, DPAs and security questionnaires?

Yes: a mutual NDA before any access, a GDPR data-processing addendum for EU clients, and your vendor security questionnaire.

Have an AI pilot that won't go live?

A 20-minute call, no slides. If an evaluation won't help, we will say so.

Book a pilot evaluation