Search that understands how people describe things.

• 01

  Natural-language product discovery

  'Something waterproof for a toddler under two thousand rupees' resolves against attributes and price, instead of returning a zero-result page.

• 02

  Grounded product Q&A

  Fit, care and compatibility answered from the spec sheet and review corpus, with the source visible so the customer can check it.

• 03

  Merchandising insight from failed searches

  Zero-result and high-exit queries clustered into a weekly list of what customers wanted and you didn't have — a buying signal most catalogues throw away.

• 01

  The catalogue is the ground truth

  Answers about price, stock and fit come from your product data at query time, never from what the model remembers about a product from training. Out-of-stock claims are a refund conversation later.

• 02

  Peak traffic is a design input

  Sale-day load is where naive AI support falls over and where costs spike hardest. We put rate limits, caching and per-conversation cost ceilings in before launch, not after the first invoice.

• 03

  Returns policy is policy, not vibes

  Eligibility is evaluated by your rules engine, with the agent explaining the outcome. A model improvising goodwill on returns is an unbudgeted liability.

1. 1

   Data inventory

   What docs / databases / APIs / feeds matter? What's PII vs public? What changes daily vs quarterly? We map it before writing code.

2. 2

   Retrieval architecture

   Hybrid (BM25 + vector) retrieval, chunking strategy, reranking, citations. Tuned for your domain not a generic benchmark.

3. 3

   Eval suite

   Real-world question set with golden answers. Faithfulness, context precision, latency. So you know when changes ship a regression.

4. 4

   Guardrails

   Prompt-injection defense, PII scrubbing, refusal patterns, output schemas. Especially critical for customer-facing deployments.

5. 5

   Production + iterate

   Cost-effective inference (caching, fallback models), monitoring, weekly eval reports. Fine-tune when the data justifies it.

• Shopify & WooCommerce
• Gorgias, Zendesk & Intercom
• Klaviyo
• Stripe & Razorpay
• Shiprocket, Delhivery & carrier APIs
• Algolia / Typesense
• GA4 & server-side tagging
• Meta & Google Ads APIs

FAQ

AI product search and catalogue RAG: your questions.

• Do we have to replace Algolia?

  No — we usually layer semantic retrieval on top and let the existing engine keep doing exact and faceted matching, which it does better and cheaper. Replacing it is a decision the numbers should force, not the architecture.

• How fresh is the stock and price data?

  Read at query time from your commerce API, never from the index. Embeddings go stale gracefully; a wrong price does not.

• Should I use RAG or fine-tuning?

  RAG for facts that change. Fine-tuning for style, format, or domain reasoning. Most production systems use both — and we'll tell you which mix is right after a 1-week discovery.

• Can I use my own LLM (open-weights)?

  Yes. We work with Llama, Mistral, Qwen, and similar — useful for data residency, cost predictability, and air-gapped environments.

• AI customer service agents for e-commerceSupport agents connected to orders, carriers and your returns rules — resolving tracking, exchanges and address changes end to end on Gorgias, Zendesk or Intercom.Read
• AI growth marketing for e-commerceServer-side tracking, margin-aware attribution and AI-assisted creative and lifecycle production — reported per SKU after returns and shipping.Read
• E-commerce prototype to productionLoad, payments, inventory correctness and observability added to a storefront or marketplace prototype before real traffic finds the cracks.Read

• RAG for banking and financial servicesRetrieval over circulars, product terms and internal procedure — returning the clause, the revision date, and a link to the page it came from.Read
• RAG for healthcare and clinical documentsRetrieval over clinical guidelines, payer policy and internal SOPs — with the citation, the version, and an explicit refusal when the library doesn't cover the question.Read
• RAG for SaaS productsRetrieval over your docs and your customers' workspace content, with isolation enforced in the index and tested adversarially.Read