── AI consulting services ──

AI Consulting Services.

AI consulting from engineers who ship. We assess your readiness, score your use cases and hand you a costed roadmap in two weeks, so the first build is the right one.

Start a project →

Free 30-minute scoping call. Written estimate within 5 working days.

2-week sprint · Fixed fee · Model-agnostic · You own the output

Illustrative interface. Values are sample data.

0+.AI systems in production
0.Days to first deploy · median
0.0%.Uptime · managed inference · 12 mo
0.0×.Cost reduction · fine-tuned vs API · median

* Sample figures for layout review. Replace with audited numbers from engagement reports before launch.

── 01 · AI consulting services ──

Eight ways we help you decide.

AI consulting is the work of deciding where AI pays off, whether your business is ready, and what to build first. Each service ends in a deliverable you can act on.

  1. 01

    AI strategy and roadmap.

    Where AI fits your business plan, what to do first, and what it will cost.

    Deliverable · Costed 12-month roadmap
  2. 02

    AI readiness assessment.

    Data, infrastructure, skills and governance, scored against what your use cases need.

    Deliverable · Readiness report
  3. 03

    Use-case discovery.

    Workshops with the teams who own the work, then every idea scored on value, feasibility and risk.

    Deliverable · Prioritised use-case matrix
  4. 04

    Generative AI consulting.

    Where LLMs help, where they don't, and which model and architecture fits each case.

    Deliverable · GenAI opportunity brief
  5. 05

    Data strategy.

    What data you have, what is missing, and the pipelines needed before any model is trained.

    Deliverable · Data gap plan
  6. 06

    Architecture and build planning.

    Target architecture, build-or-buy calls and a written estimate for the first build.

    Deliverable · Target architecture
  7. 07

    AI governance and responsible AI.

    Policies, risk tiers and controls sized to your regulators and your risk appetite.

    Deliverable · Governance framework
  8. 08

    Performance and cost reviews.

    For AI you already run: quality, drift and spend checked against what it was meant to deliver.

    Deliverable · Review report with fixes
── 02 · AI readiness assessment ──

Six scores that predict success.

Most AI projects fail on data, ownership or governance, not on the model. We score all six before anyone writes code.

Sample readiness scores out of 5: Data 2 (target 4), Infrastructure 3 (target 4), Skills 2 (target 3), Governance 1 (target 4), Use-case value 4 (target 4), Sponsorship 4 (target 5)Data · 2Infrastructure · 3Skills · 2Governance · 1Use-case value · 4Sponsorship · 4
TodayTarget for first build

  1. Data

    2 → 4 / 5

    Is the data you need available, labelled and legal to use?

  2. Infrastructure

    3 → 4 / 5

    Can your cloud and systems run and integrate a model?

  3. Skills

    2 → 3 / 5

    Who will own and operate the system after launch?

  4. Governance

    1 → 4 / 5

    Are there policies for AI risk, privacy and approval?

  5. Use-case value

    4 → 4 / 5

    Is there a workflow worth changing, with a number to move?

  6. Sponsorship

    4 → 5 / 5

    Does a business owner back it with budget and time?

* Sample scores for illustration. Your assessment scores your own data, systems and teams.

── 03 · Use-case prioritisation ──

Start with the quick wins.

Every idea from the workshops is scored on the same four criteria, then plotted. The top-right corner is where your first build comes from.

  • Value: the number it moves, and by how much
  • Data readiness: what exists, and what has to be collected
  • Risk: what happens when it is wrong, and who is affected
  • Running cost: price per request at your real volume
Sample value and feasibility scores. Quick wins: support triage, invoice extraction, contract Q&A.Strategic betsQuick winsParkFill-insFeasibility →Value →Support triageInvoice extractionContract Q&ADemand forecastSales email draftsVoice booking agentChurn model
Illustrative matrix. Bubble size shows value.
── 04 · Why Elluminati ──

Advice you can act on, including “no”.

A roadmap is only useful if it is honest about what not to build. Five commitments behind every recommendation.

  1. 01

    Model-agnostic advice.

    We pick models and vendors per task on your evaluation data, and keep switching cheap.

  2. 02

    Numbers before narratives.

    Every recommendation carries the value, cost and risk estimate behind it.

  3. 03

    We say “not yet”.

    If the data isn't ready or a tool already does the job, the report says so.

  4. 04

    Engineers in the room.

    The people advising you ship AI to production, so the plan survives contact with the build.

  5. 05

    You own the output.

    Reports, matrices and architecture are yours to use with any build partner, including your own team.

Illustrative interface. Values are sample data.

— Case studies —

The first cohort is being written.

Named case studies publish here with the client's sign-off, real numbers and the honest limits. Want to be one of them?

Talk to us →
── 05 · How a sprint runs ──

Ten working days to a roadmap.

The discovery sprint, day by day. Longer strategy programs repeat the same steps across more teams.

  1. 01 Days 1–3

    Stakeholder interviews.

    We meet the people who own the work, the data and the budget, and write down what success means.

  2. 02 Days 3–6

    Data and systems audit.

    We sample real data and map every system a model would touch.

  3. 03 Days 6–8

    Use-case scoring.

    Every idea scored on value, feasibility, risk and running cost.

  4. 04 Days 8–10

    Architecture and costs.

    Target architecture, build-or-buy calls and a cost per request for the top cases.

  5. 05 Day 10

    Roadmap readout.

    A working session with your leadership: the plan, the numbers and what happens on Monday.

── 06 · Deliverables ──

Five documents you keep.

Written for the people who will fund and build the work. Yours to use with us, another partner or your own team.

Readiness report.Scores on six dimensions, with the gaps to close first.
Use-case matrix.Every idea scored, with the top three chosen and why.
Target architecture.Systems, data flows and build-or-buy calls for each case.
Costed roadmap.Sequenced initiatives with cost, owner and exit test.
Governance framework.Risk tiers, approvals and controls sized to your regulators.
── 07 · Engagement options ──

Four ways to work with us.

Start with a fixed-fee sprint. Go further only when the roadmap says it is worth it.

Most teams start here — Discovery sprint —

Two weeks.

Fixed fee · quoted on the call

  • Readiness assessment
  • Scored use-case matrix
  • Costed roadmap and architecture
Start a project →
— Strategy program —

4–8 weeks.

Fixed fee · per phase

  • Roadmap across departments
  • Operating model and team plan
  • Board-ready business case
Talk to us
— Fractional AI lead —

Monthly.

Retainer · days per month

  • A senior consultant in your planning
  • Vendor and build reviews
  • Quarterly roadmap refresh
Book a call
— Governance review —

2–3 weeks.

Fixed fee · quoted on the call

  • AI inventory and risk tiers
  • Policies and controls
  • EU AI Act and GDPR readiness gaps
Ask about governance

* Engagement structures are placeholders pending the published rate card. Every quote is written, fixed for its scope and never guaranteed to produce a business outcome.

── 08 · Industries ──

AI consulting for your industry.

Regulators, data and margins differ by industry. The readiness scores and the roadmap format stay the same.

Mobility.

Dispatch, ETA and rider-support agents for ride-hailing and fleet platforms.

ETA models · Demand forecasting · Support agents

Logistics.

Route optimisation, document extraction and exception handling across the shipment lifecycle.

Route optimisation · Document AI · Exception triage

Finance.

KYC document extraction, fraud signals and customer assistants that stay inside compliance rules.

KYC extraction · Fraud signals · Compliance copilots

Real estate.

Listing enrichment, valuation models and lead-qualifying agents for brokerages and proptech.

Valuation · Listing enrichment · Lead agents

Retail / Manufacturing.

Demand forecasting, visual inspection and inventory copilots on the shop and factory floor.

Forecasting · Visual inspection · Inventory copilots

eCommerce / Consumer goods.

Product search, personalisation and post-purchase support agents that cite the order.

Semantic search · Personalisation · Support agents

Travel.

Itinerary assistants, dynamic pricing and disruption-handling agents for OTAs and operators.

Itinerary agents · Pricing · Disruption handling
— Not listed? —

Let's discuss your industry.

If the data is messy and the stakes are real, the method still applies. Bring the workflow; we'll bring the evaluation set.

Start a project →
── 09 · FAQ ──

AI consulting questions, answered.

Short answers here. Longer ones on the scoping call.

— Still have a question? —

Message us on WhatsApp. A consultant replies within one working day.

What does an AI consultant do?

An AI consultant helps a business decide where AI is worth using, whether it is ready, and what to build first. At Elluminati that ends in a costed roadmap, a target architecture and a scored list of use cases, written by engineers who build AI systems.

How much do AI consulting services cost?

Most engagements start with a two-week discovery sprint at a fixed fee, quoted on the scoping call. Longer strategy programs are priced per phase, and a fractional AI lead is a monthly retainer. Every quote is written and fixed for its scope.

How long does an AI consulting engagement take?

A discovery sprint takes two weeks, from the first interview to the roadmap readout. A strategy program across several departments usually takes four to eight weeks.

Will you only recommend AI you can build?

No. The report says build, buy, wait or don't for each use case, and every deliverable is yours to use with any build partner or your own team.

How do you handle AI regulation such as the EU AI Act?

A governance review builds an inventory of your AI systems, assigns risk tiers and sets the controls and documentation each tier needs, including EU AI Act and GDPR readiness gaps. It is not legal advice; your counsel signs off.

Should we build AI in-house or with a partner?

It depends on the skills you have and how often you will build. The readiness assessment scores this directly. Many teams build the first system with a partner and take over the second, with a fractional AI lead in between.

How do you keep our data private during an assessment?

We work under NDA, review data samples inside your environment rather than copying them out, and keep no copies after the engagement ends.

What happens after the roadmap?

You choose. Build the first case with us, with another partner or in-house. Many teams keep a fractional AI lead to review vendors and refresh the roadmap each quarter.

── Start a project ──

Bring the workflow. We'll bring the plan.

Three steps from first message to kickoff. No deck, no commitment until you sign.

  1. 01Scoping call30 minutes with an engineer, not a salesperson.
  2. 02Written estimateAn evaluation plan, a timeline and a price within 5 working days.
  3. 03KickoffSign the scope and the discovery sprint starts.

Prefer to message directly? WhatsApp +91 7096010005

— Start a pilot —

This is the one that decides whether a pilot is worth doing.

Nothing is stored on this website — the form opens WhatsApp with the message written out, including the pages you looked at, and you press send.

Fixed fee. Two weeks. Starts with a 30-minute call.

Start a pilot →