── Generative AI development services ──

Generative AI Development.

Copilots, document automation and content tools that write from your data, cite their sources and pass your quality checks before anyone sees the output.

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

Any model · Grounded outputs · Evaluated per release · Your cloud

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 it produces ──

Six outputs that save real hours.

Generative AI development turns models into software that drafts, extracts, answers and creates from your own data. These are the outputs we build most often.

Drafts and summaries.

Emails, reports and case summaries written in your tone, from your data, ready for a person to approve.

Output · Text

Document extraction.

Invoices, contracts, forms and IDs turned into clean fields your systems can use, with a confidence score each.

Output · Documents → data

Answers with sources.

Questions answered from your policies, manuals and tickets, with the passage each answer came from.

Output · Search

Code and SQL.

Natural-language questions turned into queries against your warehouse, reviewed before they run.

Output · Code

Images and product visuals.

Product shots, backgrounds and variants generated inside your brand rules, with a person approving what ships.

Output · Images

Meeting and call summaries.

Calls and meetings transcribed and summarised into decisions, actions and owners, straight into your CRM.

Output · Audio

* Previews show illustrative sample outputs.

── 02 · Generative AI services ──

From model choice to monthly upgrades.

Six services, one team. Each ends in something you can run, measure and own.

  1. 01

    Generative AI product development.

    Copilots, assistants and content tools built into your product or your team's daily workflow.

    Deliverable · Production GenAI app
  2. 02

    Model selection and architecture.

    Which model, which approach (prompting, RAG, fine-tuning or agents), and how it fits your stack.

    Deliverable · Architecture and cost model
  3. 03

    Fine-tuning and distillation.

    Open-weight models trained on your examples when an API model is too slow, too costly or can't leave your network.

    Deliverable · Fine-tuned model and eval report
  4. 04

    Integration and deployment.

    Your cloud, your identity provider, your APIs. Streaming, caching and rollbacks included.

    Deliverable · Deployed service
  5. 05

    Evaluation and guardrails.

    Test sets from your real cases, faithfulness and safety checks, and a release gate.

    Deliverable · Evaluation harness
  6. 06

    Upgrades and maintenance.

    New model versions tested against your evals before they ship; drift and cost watched every month.

    Deliverable · Monthly quality and cost review
── 03 · Model selection ──

The cheapest model that clears the bar.

We score candidate models on your own test set, then pick the cheapest one that meets your quality bar. Often that is a smaller model, fine-tuned on your examples.

GPTClaudeGeminiLlamaMistralQwenDeepSeekGemmaWhisperStable DiffusionFLUX
Sample model comparison: an 8B open-weight model fine-tuned on your data is the cheapest option that clears the quality bar; the large frontier model scores slightly higher at much higher cost.Your quality barDistilled 1BFrontier, smallOpen-weight 70BFrontier, largeChosen: open-weight 8B, fine-tunedCost per 1k requests →Eval score →
Illustrative comparison. Your evaluation decides the real ranking.
── 04 · Build approaches ──

Four ways to build with generative AI.

Most projects start with the simplest approach that could work, and move down the table only when the evaluation says they need to.

ApproachBest whenData you needFirst versionRunning costUpkeep
PromptingThe task is general and needs no private knowledgeA few good examples1–2 weeksPer tokenPrompt versions and evals
RAGAnswers must come from your documentsYour document corpus4–8 weeksTokens plus retrievalKeeping the corpus fresh
Fine-tuningA fixed format, tone or task at high volumeHundreds to thousands of labelled examples6–12 weeksTraining, then cheaper servingRetraining when data drifts
AgentsThe work spans several steps and systemsYour APIs plus worked examples6–12 weeksSeveral calls per taskTool and policy changes

* Typical timelines after a two-week discovery sprint. Your scope sets the real one, in the written estimate.

── 05 · Evaluation and guardrails ──

Measured before it ships.

A demo that impresses once is easy. Output you can trust ten thousand times a day needs these five things.

  1. 01

    Evaluated before release.

    A test set built from your real cases scores every prompt and model change. A regression blocks the deploy.

  2. 02

    Grounded, with sources.

    Where facts matter, outputs cite the document or record they came from, and decline when there isn't one.

  3. 03

    Guardrails on every call.

    Personal data is masked, unsafe content is filtered, and user input can't rewrite the system's instructions.

  4. 04

    Cost under control.

    Caching, routing to smaller models and per-feature budgets keep the bill predictable as usage grows.

  5. 05

    Your data stays yours.

    Built in your cloud, with providers set to zero retention. Your data never trains someone else's model.

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 ──

Generative AI for your industry.

Every industry has its own documents, language and rules. The evaluation-first method is 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 →
── 07 · Development process ──

From sample data to production.

Data first, evaluation set second, product third. 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.

── 08 · Technology stack ──

The generative AI stack we use.

Frontier and open-weight models, retrieval, serving and observability, chosen per task.

— 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 ──

Generative AI 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 is generative AI development?

Generative AI development is building software that uses large language, image or audio models to produce content: drafts, summaries, answers, code, extracted data or images. At Elluminati it includes choosing the model and approach, integrating it with your systems, and the evaluation that proves it works.

How can generative AI improve my business?

It removes the repetitive writing, reading and searching in a workflow: drafting replies and reports, pulling fields out of documents, answering questions from your policies. We agree a number to move, such as handling time or cost per case, and measure it before and after.

Do you build industry-specific or custom models?

When it pays off, yes. Most projects start with prompting or retrieval on an existing model. We fine-tune an open-weight model on your examples when you need a fixed format at high volume, lower cost, or a model that never leaves your network.

What does a generative AI project cost, and how long does it take?

Projects start with a fixed-fee discovery sprint. A retrieval-based assistant usually takes four to eight weeks to build; fine-tuning and agents take six to twelve. Each stage is priced per milestone, and every quote is written and fixed for its scope.

How do you make sure the output is accurate?

We build a test set from your real cases and score every change against it. Factual outputs are grounded in your documents with citations, and high-stakes outputs go to a person for approval before they are sent.

Will our data be used to train someone else's model?

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

Can generative AI integrate with our existing systems?

Yes. We connect models to your CRM, document stores, data warehouse and internal tools through their APIs, with SSO, role-based access and an audit log of every model call.

What security and compliance standards do you follow?

Encryption in transit and at rest, SSO, role-based access and full audit logs as standard. Systems are built to support GDPR, HIPAA and SOC 2 controls, and your counsel signs off on the compliance position.

── 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.

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