── AI development services ──

Custom AI, built to ship.

We build LLM apps, agents, retrieval systems and ML models in stages, from proof of concept to production. Each stage has an exit test, so you only fund the next one when the numbers say so.

Start a project → See the stages

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

PoC in ~4 weeks · Your cloud · Any model · Evaluation-first

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 · What we build ──

Six kinds of AI, built end to end.

AI development is the engineering work of turning a model into software a business relies on: data, evaluation, integration, security and operations, not just the model.

LLM apps & copilots

Assistants and copilots inside the tools your team already uses, on any model.

AI agents & assistants

Multi-step agents, chatbots and voice agents that call your APIs, with approval gates.

RAG & knowledge systems

Cited answers over your contracts, manuals, tickets and wikis, permission-aware.

Custom ML & fine-tuned models

Forecasting, scoring and fine-tuned open-weight LLMs, served where you need them.

Computer vision

Detection, OCR and inspection pipelines, on edge devices or in the cloud.

Integration & automation

AI steps wired into CRM, ERP and back-office flows, replacing brittle RPA scripts.
── 02 · From PoC to production ──

Four stages, each with an exit test.

You only fund the next stage when the numbers say so. Most AI projects stall between a promising demo and a system people use; this is how we cross that gap.

  1. Does it work on your data?

    A narrow prototype on real examples, scored against an evaluation set we build with you.

    — Exit test —
    Beats the agreed accuracy threshold on held-out cases.
    • Working prototype
    • Evaluation report
    • Go / no-go recommendation

  2. Does it work for real users?

    One team, live traffic, a guarded rollout. We measure quality, latency and cost per task in the wild.

    — Exit test —
    Pilot users hit the target on live traffic, within the cost budget.
    • Pilot deployment in your cloud
    • Feedback and review loop
    • Cost model per request

  3. Can the business rely on it?

    Hardened, integrated and monitored, with access control, audit logs and a rollback path.

    — Exit test —
    Service levels met for 30 days, runbooks signed off.
    • Production deploy and integrations
    • Monitoring and alerts
    • Runbooks and team handover

  4. What else can it do?

    More workflows on the same harness, cheaper models where evals allow, and a monthly review.

    — Exit test —
    Ongoing: quality, cost and drift reviewed every month.
    • Model upgrades through your evals
    • New use cases on the same stack
    • Monthly cost and quality review

* Typical timeline for a single workflow. Your scope sets the real one, in the written estimate.

Already have a prototype? We'll run it against an evaluation set and tell you, in writing, what it takes to reach production.

Get a prototype review
── 03 · AI technologies ──

Ten AI technologies, one engineering standard.

Every one ships with an evaluation set, monitoring and a cost line, whichever model or framework sits underneath.

TechnologyWhat we buildTypical first deliverable
Machine learningForecasting, scoring and ranking models on your tabular dataA model that beats your current baseline
Generative AIDrafting, summarising and structured extraction with LLMsA copilot inside one tool your team uses
Agentic AIMulti-step agents that call your APIs, with approval gatesOne workflow automated end to end
RAGAnswers grounded in your documents, with citationsA knowledge assistant over one corpus
NLPClassification, routing, entity extraction and semantic searchA ticket or document classifier
Computer visionDetection, OCR and visual inspectionA pipeline scored on your images
Speech & voiceTranscription, voice agents and call analyticsA voice agent on one call type
Edge AIOptimised models on cameras, devices and on-prem GPUsA model running on your hardware
Intelligent automationAI steps that replace brittle RPA scripts, with fallbacksOne back-office process automated
Explainable AIReason codes, model cards and decision audit trailsExplanations your risk team signs off
── 04 · Risk controls ──

Risk controls built in, not bolted on.

The controls your security and compliance teams will ask about are part of the first sprint, not a pre-launch scramble.

  1. 01

    Hallucination control.

    Retrieval with citations, faithfulness checks on every answer, and a refusal when the sources don't support a claim.

  2. 02

    Data protection.

    PII is redacted before any prompt leaves your network. Model providers are set to zero retention.

  3. 03

    Prompt-injection defence.

    Untrusted input is isolated from instructions, tool calls are allow-listed, and risky actions need a human.

  4. 04

    Access and audit.

    SSO, role-based access and a log of every model call, so security and compliance can trace any decision.

  5. 05

    Compliance by design.

    Architecture and documentation built to support GDPR, HIPAA and EU AI Act obligations, with your counsel signing off.

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 ──

AI development for your industry.

The data and the regulations change from one industry to the next. The method stays the same: an evaluation set first, then a system your 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 a build runs ──

How a build runs, week by week.

The same six steps behind every stage above, with 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 stack ──

The stack we build on.

Frontier and open-weight models, chosen per task on your evaluation set, on the cloud you already use.

— 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 ──

AI development questions, answered.

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 does an AI development company do?

An AI development company designs, builds and runs software that uses machine learning or large language models. At Elluminati that means taking a use case from a proof of concept to a production system, with the evaluation, monitoring and integrations it needs to stay reliable.

How much does it cost to develop an AI application?

It depends on scope, data and where the system runs. Most projects start with a fixed-fee discovery sprint, quoted on the scoping call. After that, each stage is priced per milestone, and every quote is written and fixed for its scope.

How long does it take to build an AI product?

A proof of concept typically takes about four weeks. A pilot with real users follows, and a production system is usually live around week 14. Your scope sets the real timeline, and you decide at each stage whether to continue.

When should we build custom AI instead of buying a tool?

Buy when an off-the-shelf tool already fits the workflow. Build when the workflow is specific to your business, the data can't leave your network, or the per-seat cost of a tool grows faster than the value. The discovery sprint answers this before you commit to a build.

How do you prevent hallucinations?

We ground answers in your documents with retrieval and citations, check every answer for faithfulness against its sources, and make the system refuse when the sources don't support a claim. Faithfulness is measured on a held-out set before each release.

Is our data safe during development?

Yes. We build in your cloud account, redact personal data before prompts leave your network, configure model providers for zero retention, and keep no copy of your data after handover.

Can you add AI to our existing systems?

Yes. Most builds connect to software you already run, such as CRM, ERP, helpdesk and data warehouses, through their APIs or events, with SSO, role-based access and audit logging.

What support do you provide after deployment?

You choose between a handover with runbooks and a trained team, or a managed run: monitoring, drift alerts, model upgrades tested against your evals, 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 →