The knowledge brain behind every dealer conversation.
RAG is what separates a reliable enterprise sales agent from a hallucinating chatbot. Every scheme explanation, every objection answer, every policy guardrail — grounded in Berger's own verified knowledge, retrieved in real time, during a live voice call.
Directional targets for the MVP business case — to be baselined against a controlled pilot before rollout.
Scheme terms change. Dealer questions don't wait.
A sales agent that answers from memory alone will give yesterday's scheme details, wrong eligibility criteria, and make-up answers under objection pressure. RAG fixes this by retrieving verified, up-to-date knowledge at the moment of the question.
Agent retrieves before it answers
- — Scheme details fetched live from the knowledge store
- — Eligibility rules sourced from MCC/Oracle-synced data
- — Objection answers matched to approved policy documents
- — Every retrieved chunk logged for full auditability
Knowledge improves after every call
- — Failed objection answers flagged and reviewed
- — New scheme terms ingested within the same cycle
- — Retrieval ranking tuned from call outcome data
- — Business users update knowledge without retraining the model
LLM answers from training data alone
- · Scheme details frozen at model training time
- · Eligibility rules guessed, not retrieved
- · Objection answers may contradict policy
- · No audit trail for what knowledge was used
From dealer question to grounded answer in five steps.
This pipeline runs entirely within the latency budget of a live phone call. The dealer never knows retrieval happened — they just get a confident, accurate answer.
Four layers of verified knowledge.
The RAG knowledge store is not a static document dump. Each layer has a defined source, an update frequency, and an owner inside Berger.
Scheme Knowledge
- — Scheme names, mechanics, and qualifying criteria
- — Dealer eligibility rules by segment, market, and depot
- — Scheme balance, qualification threshold, and progress tracking
- — Approved pitch language and key selling points per scheme
- — Common dealer questions and approved answers per scheme
Objection Playbook
- — Categorised objection types and recommended responses
- — Price objection handling with competitive context
- — Scheme complexity objections answered in plain language
- — Timing objections with urgency and deadline framing
- — Escalation triggers: when to transfer to a human agent
Product & Catalog Knowledge
- — Product names, categories, and SKUs
- — Pack sizes, order units, and minimum order quantities
- — Product features, formulations, and application guidance
- — Substitution rules and related SKU recommendations
- — Catalog constraints used in order validation
Policy & Compliance Guardrails
- — What the agent is permitted to commit to on a call
- — Pricing and discount communication boundaries
- — Data handling and consent language for voice calls
- — Regulatory requirements for scheme communication
- — Fallback language when a question is out of policy bounds
Real dealer questions. Grounded answers.
These are the structured sales patterns the agent runs during a scheme call. Each defines the trigger phrase, what knowledge is retrieved, the strict guardrails, and the exact answer the agent must produce — no improvisation, no invented scheme terms. The payment-intelligence patterns already proven in the POC carry over as the same retrieval discipline.
The same retrieval discipline already runs live in the POC for payment-intelligence queries — PD invoices, pending invoices, credit/debit notes, payment modes, and outstanding balance — with totals sourced strictly from database aggregation, never LLM calculation. The sales patterns above extend that proven pattern to scheme selling.
One verified answer. Nine languages.
A dealer in Tamil Nadu asks in Tamil. A dealer in Bengal asks in Bengali. Both must receive the same approved scheme answer — not a different one, not a mistranslated one. Here is how the RAG layer keeps knowledge consistent across all nine languages.
Update a scheme answer once, and every dealer in every region hears the corrected, compliant version — no nine-way translation project, no regional inconsistency, no compliance gap between languages.
Berger's data stays Berger's.
Dealer data, scheme terms, and order records are commercially sensitive. The RAG layer is architected so this knowledge never has to leave Berger's control.
In-Region Hosting
Knowledge store and vector index can be hosted in Indian data centres to meet residency requirements.
Self-Hostable LLM
Can run on a private Llama-style model so dealer and scheme data is never sent to a third-party API.
Full Audit Trail
Every retrieved chunk and every answer is logged — inspectable on demand for compliance review.
Access-Controlled
Role-based access governs who can read, update, or approve knowledge in the store.
The RAG layer gets smarter after every call.
Most AI systems require an ML team to improve them. Berger's RAG layer is designed so that business users — not engineers — can update approved answers, retire outdated scheme documents, and tune retrieval performance within the same call cycle.
Call completes
Dealer conversation ends. Outcome classified: converted, objection, retry, or failed.
Gaps are flagged
Unanswered questions, failed objections, and wrong extractions are tagged automatically for review.
Knowledge is updated
Trade marketing or sales ops corrects the relevant scheme document or objection answer in the knowledge store.
Next call is better
The updated document is re-indexed. The next dealer asking the same question gets the corrected, approved answer.
Why RAG is non-negotiable for Berger.
Brand Protection
A hallucinated scheme commitment made on a call is a legal and commercial liability. RAG ensures the agent never says something Berger hasn't approved.
Scheme Conversion
Dealers convert on scheme calls when answers are fast and credible. RAG gives the agent instant access to exact scheme terms, balance, and pitch — no hesitation.
Ops Agility
New scheme every quarter. New objection every week. RAG means Berger's business team can update the agent's knowledge without touching code or retraining a model.
Audit Readiness
Management or regulatory review of any call will show exactly which document was retrieved, which chunk was used, and what response was generated.
Dealer Trust
Dealers who receive consistent, policy-accurate answers across calls learn to trust the agent. Inconsistent AI answers destroy that trust permanently.
Institutional Knowledge
The RAG store becomes Berger's living sales intelligence — the best objection answers, sharpest scheme pitches, curated from every call, every cycle.