── AI services and solutions ──

AI services for production.

Consulting, LLM apps, agents, retrieval, custom models and MLOps from one engineering team. Every system is scored on your own data before it ships, and runs in your cloud.

Start a project →

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

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 · Overview ──

AI that runs inside your operations.

Elluminati's AI services and solutions cover the full path from a scoped use case to a system in production, and the work of keeping it there.

  • Added to the software, data and cloud you already run
  • Custom models and LLM apps, evaluated on your own examples
  • Monitoring, drift alerts and cost tracking from day one
  • Access control and audit trails designed in, not bolted on
  • Upgrades, retraining and a monthly review after launch
── 02 · Our AI services ──

Eight services. One accountable team.

Pick one or combine them. Every engagement ships with an evaluation set, a cost dashboard and a runbook.

— 01 · AI consulting —

A roadmap you can fund, not a slide deck.

We score your candidate use cases on value, data readiness and risk, then turn the strongest into a costed plan your leadership can approve.

  • Use-case discovery and scoring workshops
  • Data readiness and infrastructure audit
  • Build, buy or fine-tune advice, with cost per request
  • Governance, risk and compliance framework
— You walk away with —

A prioritised roadmap, a target architecture and a written estimate for the first build.

2–4 weeksFixed fee Scope a roadmap →

Illustrative interface. Values are sample data.

— 02 · Generative AI apps —

Copilots your team will actually open.

LLM applications on any model: internal copilots, customer assistants and content pipelines, with streaming, memory, guardrails and a cost line per feature.

  • Copilots inside CRM, ERP, helpdesk and internal tools
  • Structured generation for documents, emails and reports
  • Guardrails for PII, tone, policy and prompt injection
  • Prompt and model versioning, with evals on every change
— You walk away with —

A production LLM app in your cloud, with an eval suite and a cost dashboard.

4–10 weeksPer milestone Scope an LLM app →

Illustrative interface. Values are sample data.

— 03 · AI agents —

Agents that do the work and show the trace.

Multi-step agents that call your tools and APIs, pause for human approval where it matters, and log every decision so it can be replayed.

  • Tool calling against your CRM, ERP, ticketing and APIs
  • Approval gates on steps that spend money or send messages
  • Voice agents for inbound calls and scheduling
  • Replayable traces for debugging, QA and audit
— You walk away with —

An agent live on one workflow, with approval rules and a trace for every run.

6–12 weeksPer milestone Scope an agent →

Illustrative interface. Values are sample data.

— 04 · RAG & knowledge —

Answers from your documents, with citations.

Retrieval over contracts, manuals, tickets and wikis. Every answer points to its source passage, and every miss is logged so the corpus improves.

  • Hybrid search: dense, keyword and reranking, tuned on your queries
  • Permission-aware retrieval that respects existing access
  • Ingestion for PDFs, wikis, shared drives and ticketing tools
  • Faithfulness and recall measured on a held-out set
— You walk away with —

A cited-answer system over your corpus, with recall and faithfulness tracked per release.

4–8 weeksPer milestone Scope a knowledge system →

Illustrative interface. Values are sample data.

— 05 · ML & fine-tuning —

Custom models trained on your data.

Forecasting, classification and ranking models, plus fine-tuned open-weight LLMs when an API model is too slow, too costly or can't leave your network.

  • Predictive models for demand, churn, pricing and risk
  • Fine-tuning open-weight models on your labelled examples
  • Data curation, label QA and synthetic augmentation
  • Side-by-side evals against your current baseline
— You walk away with —

A model scored against your baseline on your own held-out set, served where you need it.

6–12 weeksPer milestone Scope a model →

Illustrative interface. Values are sample data.

— 06 · Computer vision —

Cameras and documents, turned into data.

Detection, OCR, inspection and video pipelines for shop floors, warehouses and back offices, deployed to edge devices or the cloud.

  • Object detection and counting on images and video
  • OCR and layout extraction for invoices, IDs and forms
  • Visual quality inspection on production lines
  • Edge deployment on cameras and on-prem GPUs
— You walk away with —

A vision pipeline scored on your images, running on the hardware you have.

6–12 weeksPer milestone Scope a vision pipeline →

Illustrative interface. Values are sample data.

— 07 · Integration & automation —

AI inside the systems you already run.

We connect models to your existing software and automate the repetitive steps around them, so AI shows up where your team already works.

  • API and event integrations with CRM, ERP and warehouses
  • Workflow automation that replaces brittle RPA scripts
  • SSO, role-based access and audit logging
  • Rollouts that run old and new paths side by side
— You walk away with —

AI running inside one production workflow, with a tested rollback path.

4–8 weeksPer milestone Scope an integration →

Illustrative interface. Values are sample data.

— 08 · MLOps & governance —

Monitoring and audit for AI in production.

For AI you already run, or AI we build: evaluation in CI, drift and cost alerts, model upgrades and the documentation your risk team asks for.

  • An evaluation harness that blocks a deploy on regression
  • Drift, latency and cost monitoring with alerts
  • Model cards, audit logs and responsible-AI reviews
  • On-call, upgrades and a monthly quality and cost review
— You walk away with —

A dashboard and runbook your team can operate, or a managed run by us.

OngoingMonthly Talk about MLOps →

Illustrative interface. Values are sample data.

Not sure which service fits? Book a 30-minute scoping call. We'll tell you where to start, or whether AI is the wrong tool.

Book a call
── 03 · Solutions by business function ──

Nine problems AI solves well today.

Each one starts with a workflow and a number to move. Colours match the service that builds it.

Customer support assistants.

Answer, triage and draft replies from your help centre and order data, then hand complex cases to a person with the context attached.

AgentsRAG

Document processing.

Pull fields from invoices, contracts, claims and KYC documents, with a review queue for low-confidence cases.

VisionLLMs

Internal knowledge search.

Answers from policies, runbooks and past tickets, with the source passage cited.

RAG

Sales and lead qualification.

Score inbound leads, enrich records and draft first replies for your reps.

AgentsML

Recommendations.

Rank products, content or next actions, and measure lift against your current rules.

ML

Fraud and risk signals.

Flag unusual transactions and claims as they happen, with a reason for the reviewer.

ML

Forecasting.

Demand, inventory, staffing and cash forecasts, with error bands planners can act on.

ML

Visual inspection.

Detect defects, count stock and watch sites from cameras, on edge devices.

Vision

Back-office automation.

Route, reconcile and update records across finance, HR and ops, with approval gates.

AgentsIntegration

* Chat shown is illustrative sample data.

── 04 · Why Elluminati ──

Five reasons teams pick us.

We are a delivery team, not a strategy deck. These commitments hold on every engagement.

  1. 01

    Evaluation-first.

    We agree what “working” means as a number before we build. Every release is scored against it, and a regression blocks the deploy.

  2. 02

    Your data stays yours.

    Systems run in your cloud account. Model providers are set to zero retention, and we keep no copy after handover.

  3. 03

    Model-agnostic.

    We pick the model per task on your evals, not on a vendor relationship. Switching to a cheaper one is a config change.

  4. 04

    Cost you can see.

    Tokens, GPU hours and vendor bills roll up to one number per feature, with a budget alert before finance gets a surprise.

  5. 05

    Engineers from day one.

    The engineers on the scoping call build the system and can run it after launch, so context isn't lost between teams.

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 · Industries ──

Seven industries, one method.

Different data, same approach: an evaluation set first, then a system your operations team can trust.

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 →
── 06 · How an engagement runs ──

Six steps from idea to production.

Three phases, six steps, and a working demo every Friday once the build starts.

  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.

── 07 · Technology ──

Model-agnostic and cloud-agnostic.

We choose tools per task on your evaluation set, and keep the swap cheap.

— 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.

── 08 · FAQ ──

Questions buyers ask first.

Short answers here. Longer ones on the scoping call.

— Still have a question? —

Message us on WhatsApp. An engineer replies within one working day.

What are AI services and solutions?

AI services are the work of designing, building and running AI systems for a business: consulting, LLM applications, agents, retrieval, custom models, integration and MLOps. A solution is the result, such as a support assistant or a document pipeline, running inside your workflow.

How much does an AI project cost?

It depends on scope, data and where the system runs. Most engagements start with a two-week discovery sprint at a fixed fee, quoted on the scoping call. Builds are priced per milestone, and every quote is written and fixed for its scope.

How long until something is in production?

Discovery takes two weeks. Builds typically run 4 to 12 weeks, with a working demo every Friday, so you see progress against the evaluation set long before launch.

Will our data be used to train AI models?

No. We build in your cloud account, configure model providers for zero data retention, and keep no copy of your data after handover.

Can you add AI to our existing software?

Yes. Most of our work connects models to systems you already run, such as CRM, ERP, helpdesk and data warehouses, through their APIs, with SSO, role-based access and audit logging.

How do we know if we're ready for AI?

You need a repeatable workflow, some real examples of it, and a way to judge a good result. The discovery sprint checks all three, and we will tell you if AI is the wrong tool for the job.

What happens after launch?

You choose. We hand over with runbooks and a trained team, or we run the system for you with monitoring, drift alerts, model upgrades and a monthly cost and quality review.

── 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 →