── Machine learning consulting services ──

Know if ML will pay off first.

Feasibility checks, ML strategy, MLOps assessments and model reviews from engineers who build ML in production, so you invest in the models that will pay off.

Start a project → Is ML the right tool

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

Feasibility first · Model-agnostic · Written recommendations · 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 · Machine learning consulting services ──

Six services before, during and after the build.

Machine learning consulting is deciding where ML pays off, whether your data can support it, and how to run it reliably, before and while you build.

  1. 01

    ML strategy.

    Where ML fits your business plan, which problems to start with, and what they're worth.

    Deliverable · ML strategy and roadmap
  2. 02

    Feasibility and data audits.

    Whether your data can support the model you want, before anyone builds it.

    Deliverable · Feasibility report
  3. 03

    Model development strategy.

    The method, baseline, evaluation plan and team needed for each model.

    Deliverable · Model plan
  4. 04

    MLOps assessment.

    How models are trained, deployed and monitored today, and what to automate next.

    Deliverable · MLOps maturity report
  5. 05

    Model reviews.

    An independent check of models you already run: accuracy, fairness, drift and cost.

    Deliverable · Model review report
  6. 06

    ML engineering guidance.

    Architecture and code reviews for your team while they build.

    Deliverable · Architecture review
── 02 · Feasibility ──

Check before you build.

Many ML projects fail because they shouldn't have started. We answer four questions first, in writing.

  • A written go or no-go, with the reasons
  • The baseline any model must beat
  • The data work needed before a build
  • A cost and value estimate for the first model
Decision tree: build an ML model only if there is a pattern in past data, enough labelled examples, a simple baseline falls short, and someone will act on the prediction. Otherwise use rules, collect data, keep the baseline or fix the process first.Is there a pattern in past data?An outcome that repeatsUse rules or analyticsML has nothing to learnEnough labelled examples?Usually thousands of rowsCollect or label data firstThen check againDoes a simple baseline fall short?Rules, averages, heuristicsKeep the baselineCheaper and easier to explainWill someone act on it?A named owner and a decisionFix the process firstA model nobody uses is wasteBuild an ML modelStart with a 4-week proofNoNoNoNoYesYesYesYes
Scroll sideways to see the full diagram →
── 03 · MLOps maturity ──

Four levels of MLOps maturity.

Most teams are at level 0 or 1. The assessment shows where you are and the cheapest next step up.

  1. Notebooks and hand-offs.

    Models are trained by hand and copied to production. Nobody is sure which version is live.

    — Next step —
    Every model in version control, with its data.
    • Version control
    • Reproducible training
    • A named owner

  2. Automated training.

    Training runs as a pipeline, and results are logged and comparable.

    — Next step —
    Deployment without manual steps.
    • Training pipelines
    • Experiment tracking
    • Evaluation reports

  3. CI/CD for models.

    Every change is tested and deployed automatically, with rollback.

    — Next step —
    Monitoring that catches drift.
    • Model registry
    • Automated tests
    • One-click rollback

  4. Monitored and retrained.

    Drift and accuracy are watched in production, and retraining is triggered automatically.

    — Next step —
    Keep improving, reviewed quarterly.
    • Drift monitoring
    • Automated retraining
    • Cost tracking
── 04 · Engagement options ──

Start small, go further when it pays.

Start with a fixed-fee feasibility sprint. Go further only when the numbers say it's worth it.

Most teams start here — ML feasibility sprint —

Two weeks.

Fixed fee · quoted on the call

  • Data audit
  • Baseline and go / no-go
  • Cost and value estimate
Start a project →
— ML strategy —

4–6 weeks.

Fixed fee · per phase

  • Portfolio of ML use cases
  • Team and platform plan
  • Roadmap with business cases
Talk to us
— MLOps assessment —

2–3 weeks.

Fixed fee · quoted on the call

  • Maturity score
  • Platform and tooling review
  • Prioritised automation plan
Book a call
— Fractional ML lead —

Monthly.

Retainer · days per month

  • Model and code reviews
  • Hiring and vendor support
  • Quarterly roadmap refresh
Ask about retainers

* 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.

── 05 · Why Elluminati ──

Advice from people who ship ML.

Recommendations are only as good as the experience behind them. Five commitments on every engagement.

  1. 01

    Feasibility before code.

    We tell you in writing when ML isn't the answer, and what to do instead.

  2. 02

    Engineers, not slideware.

    The consultants advising you build and run ML systems in production.

  3. 03

    Baselines first.

    Every recommendation names the baseline a model must beat, and by how much.

  4. 04

    Model-agnostic.

    Methods and tools are chosen on your data and constraints, not on vendor relationships.

  5. 05

    You own the output.

    Reports, code reviews and plans are yours to use with any team or partner.

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 →
── 06 · Industries ──

ML consulting for your industry.

Any business with repeated decisions and historical data can use ML. The feasibility questions are the same everywhere.

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 →
── 07 · How we work ──

From first question to running models.

Consulting and delivery follow the same steps, so a recommendation turns into a build without starting over.

  1. 01 Discovery · week 1

    Workflow and data audit.

    We map the process, pull a sample of real data, and check what is usable, missing or sensitive.

  2. 02 Discovery · week 2

    Evaluation set.

    We build the test set from your real examples, so “done” has a number before any product code exists.

  3. 03 Build · sprint 1

    Architecture and prototype.

    Model choice, retrieval design and integration plan, proven against the eval set.

  4. 04 Build · every Friday

    Build in the open.

    Your repository, your cloud. A weekly demo with accuracy, latency and cost on one screen.

  5. 05 Run · go-live

    Deploy.

    A staged rollout behind a feature flag, with runbooks, monitoring and a rollback path.

  6. 06 Run · monthly

    Monitor and improve.

    Drift alerts, model upgrades run through your evals, and a monthly cost and quality review.

── 08 · Technology stack ──

The ML stack we assess and build on.

Model-agnostic tools for data, training, serving and monitoring, chosen to fit your cloud and your team.

— Models —
  • OpenAI
  • Anthropic
  • Gemini
  • Llama
  • Mistral
  • Qwen
— Agents & orchestration —
  • LangChain
  • Temporal
  • FastAPI
  • Python
— Retrieval & data —
  • pgvector
  • Pinecone
  • Elasticsearch
  • Redis
  • Databricks
  • Snowflake
— Training & serving —
  • PyTorch
  • Hugging Face
  • vLLM
  • NVIDIA TensorRT
  • ONNX
  • OpenCV
— MLOps & observability —
  • MLflow
  • Weights & Biases
  • Grafana
  • Prometheus
  • OpenTelemetry
— Cloud & infrastructure —
  • AWS
  • Google Cloud
  • Azure
  • Kubernetes
  • Terraform
  • Docker

* Tools we build with. No vendor partnership or endorsement is implied.

── 09 · FAQ ──

ML 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 a machine learning consultant do?

An ML consultant helps you decide which problems suit machine learning, whether your data can support a model, and how to build and run it reliably. At Elluminati the consultants are engineers who build ML in production.

How much does ML consulting cost?

Most engagements start with a two-week feasibility sprint at a fixed fee, quoted on the scoping call. Strategy work is priced per phase and a fractional ML lead is a monthly retainer; every quote is written and fixed for its scope.

How can ML consulting help us scale?

By choosing the use cases with real returns, fixing the data and MLOps gaps that slow every project down, and setting up practices that let your team ship models without starting from scratch each time.

What's the difference between ML consulting and ML development?

Consulting decides what to build and how; development builds and runs it. Many clients start with consulting and continue with development, with us or with another team.

Can you review models we already run?

Yes. A model review checks accuracy against a baseline, data leakage, fairness, drift monitoring and cost, and ends in a written report with fixes in priority order.

Which industries benefit most from ML consulting?

Any business with repeated decisions and historical data. Retail and eCommerce, logistics, finance, insurance, manufacturing, travel and real estate are common.

What does the feasibility sprint deliver?

A written go or no-go, the baseline to beat, the data work needed, and a cost and value estimate for the first model.

Do you help us hire an ML team?

Yes. A fractional ML lead can define roles, review candidates and vendors, and set up the practices a new team needs.

── 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 →