── RAG development services ──

RAG Development Services.

Retrieval-augmented generation over your contracts, manuals, tickets and wikis. Every answer cites its source, respects who can see what, and is measured before release.

Start a project →

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

Cited answers · Permission-aware · Measured recall · 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 · RAG development services ──

Six services, one retrieval pipeline.

RAG development is building systems that retrieve the right passages from your documents and have a language model answer from them, with citations.

  1. 01

    RAG consulting.

    Which questions RAG should answer, which sources it needs and how accuracy will be measured.

    Deliverable · RAG plan and evaluation set
  2. 02

    Custom RAG development.

    Ingestion, chunking, hybrid search, reranking and generation built for your documents.

    Deliverable · Production RAG system
  3. 03

    RAG evaluation.

    Recall, faithfulness and answer quality measured on your real questions, every release.

    Deliverable · Evaluation harness
  4. 04

    RAG integration.

    Answers delivered in Slack, Teams, your intranet, helpdesk or product.

    Deliverable · Integrated assistant
  5. 05

    RAG applications.

    Support assistants, research tools and policy search built on the same pipeline.

    Deliverable · RAG application
  6. 06

    Retrieval tuning.

    Embedding models and rerankers tuned on your queries when standard search falls short.

    Deliverable · Tuned retriever
── 02 · How RAG works ──

Two pipelines, one answer.

Documents are ingested and indexed as they change. Each question searches that index, reranks what it finds and answers only from the best passages.

RAG pipeline: sources are parsed, chunked, embedded and indexed as they change. Each question runs a permission-filtered hybrid search, the results are reranked, and the answer is generated only from the best passages, with citations.Your sourcesPDFs · wikis · ticketsParse and chunkLayout-awareEmbed and indexVector + keywordUser questionWith user identityHybrid searchPermission-filteredRerankBest 5 passagesAnswer with sourcesCited, or declinesIngest · runs when documents changeQuery · runs for every question
Scroll sideways to see the full diagram →
── 03 · Retrieval quality ──

Most RAG failures are search failures.

If the right passage isn't retrieved, no model can answer correctly. We measure retrieval on its own, and tune it first.

  • Hybrid search: dense vectors plus keyword matching
  • A reranker that reads the top candidates before answering
  • Chunking that follows your documents' structure
  • Recall and faithfulness tracked on your own questions
— Recall at k · 400 real questions — Sample recall at k: hybrid search with reranking finds the right passage in the top 5 for 94% of questions, against 78% for dense search alone.0.50.751k=1235810Recall
Illustrative sample results.
── 04 · RAG or fine-tuning? ──

When RAG is the right approach.

RAG, fine-tuning and long context windows solve different problems, and many systems combine them.

RAGFine-tuningLong context
Best forAnswers from facts that changeA fixed style, format or taskA few long documents at once
Keeping knowledge freshRe-index when documents changeRetrain to updateSend again with each request
Cites its sourcesYesNoPartly
Respects permissionsYes, filtered at searchNoOnly if you filter first
Cost per answerLowLowest at high volumeHigh for large inputs
── 05 · Security and trust ──

Answers people can check.

A wrong answer delivered confidently is worse than no answer. These controls ship with the first version.

  1. 01

    Permission-aware retrieval.

    Search is filtered by the asker's own access rights, so no one sees a passage they couldn't open.

  2. 02

    Citations on every answer.

    Each claim links to its source passage. With no source, the system says it doesn't know.

  3. 03

    Fresh content.

    Sources are re-indexed when they change, and stale documents can be retired in one step.

  4. 04

    Personal data masked.

    Personal data is detected and masked before it reaches the model, and in the logs.

  5. 05

    Compliance support.

    Built to support GDPR, HIPAA and SOC 2 controls, with your security team and 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 →
── 06 · Industries ──

RAG for your industry.

Every industry has its own documents and rules. Retrieval, citation and evaluation work the same way.

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 document audit to live answers.

Discovery starts with your documents and the questions people actually ask. The evaluation set comes from those questions.

  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 RAG stack we build on.

Vector and keyword search, embedding and reranking models, and any LLM, 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 ──

RAG 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.

How much does it cost to build a RAG system?

Projects start with a fixed-fee discovery sprint, quoted on the scoping call. The build is priced per milestone, and every quote is written and fixed for its scope.

How long does it take to develop a RAG system?

After a two-week discovery sprint, a first RAG system usually takes four to eight weeks. More sources, stricter permissions or many languages add time, and the estimate says so.

When is RAG better than fine-tuning?

When answers depend on facts that change, when you need citations, or when different users may see different documents. Fine-tuning suits a fixed style or task, and the two can be combined.

What data sources work with RAG?

PDFs, Word and slide files, wikis, shared drives, ticketing systems, CRMs, databases and websites. Scanned documents are read with OCR first.

How do you measure RAG accuracy?

We measure retrieval recall, faithfulness of answers to their sources, and answer correctness on a set of your real questions with agreed answers, for every release.

How do you stop it making things up?

Answers are generated only from retrieved passages, each claim is checked against them, and the system declines when the sources don't support an answer.

Does RAG respect document permissions?

Yes. Retrieval is filtered by the asker's access rights from your identity provider, so answers only draw on documents that person could open.

What does a RAG system cost to run?

Mostly model tokens and the search index. Caching, smaller models for simple questions and a reranker that keeps prompts short keep the cost per answer low.

── 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 →