Global: TP ICAP Enhances CRM Insights Using Amazon Bedrock and RAG Tech

Shoppers of enterprise AI are discovering how TP ICAP turned thousands of dusty CRM meeting notes into instant, actionable answers with Amazon Bedrock , a fast, secure way to build Retrieval Augmented Generation (RAG) and text-to-SQL workflows that cut research time from hours to seconds and keeps audit trails intact.

• Speed boost: ClientIQ reduced time spent on research tasks by around 75%, turning manual searches into near-instant results.
• Hybrid retrieval: Uses metadata pre-filtering plus semantic search for precise, context-rich document hits.
• Two-path query engine: LLM routes plain-English questions to either RAG for notes or text-to-SQL for structured analytics.
• Enterprise security: Integrates Salesforce permissions via Okta claims so answers respect existing access controls.
• Automated quality checks: Bedrock Evaluations are part of CI/CD, measuring relevance, factual accuracy and retrieval precision.

How TP ICAP made meeting notes useful again, fast and with a human touch

TP ICAP’s problem was one many companies recognise , tens of thousands of qualitative meeting records in Salesforce that held valuable context but were effectively invisible. The Innovation Lab wanted quick, trustworthy answers rather than pages of hit-or-miss search results. The result, ClientIQ, feels like talking to a knowledgeable colleague: it summarises, cites sources and links back to the original Salesforce record so you can verify the claim and follow up.

There’s a sensory payoff here too , responses read naturally and include direct links to records, so the output doesn’t feel like a vague AI hallucination. Internally, users have said searches are noticeably less tedious and insights arrive with the context they need.

Why a two-path approach (RAG plus text-to-SQL) beats a one-size-fits-all assistant

Rather than forcing every question through a general LLM, ClientIQ first classifies the user’s intent. If the question concerns unstructured meeting notes, a RAG workflow retrieves relevant documents and builds a context-aware answer. If it’s an analytical or tabular request, the system generates SQL to query Athena and returns concrete figures. That means you get narrative insight when you want it and hard numbers when you need them, without confusing the two.

This split also helps control cost and latency , TP ICAP can run smaller, cheaper models where appropriate and only use heavier generation when needed. It’s a pragmatic design if you care about both speed and accuracy.

How hybrid search and metadata tagging lift retrieval from “noisy” to “helpful”

ClientIQ uses hybrid searching: metadata filters narrow the universe first, then semantic embeddings find the best matches within that subset. For instance, asking for “executive meetings with AnyCompany in Chicago” first filters for Visiting_City_C = Chicago then performs semantic matching, avoiding irrelevant hits from other divisions or cities.

TP ICAP improved retrieval quality by custom chunking during ingestion , one CSV per meeting, enriched with topic tags generated by Nova Pro. Embeddings (Amazon Titan v1) index each meeting in OpenSearch Serverless, and Bedrock Knowledge Bases handle session context and source attribution so answers stay grounded and traceable.

Why Amazon Bedrock made the build faster and more flexible

TP ICAP could have used a CRM vendor’s in-built assistant but chose Amazon Bedrock for flexibility, model choice, and managed services. Bedrock exposes multiple foundation models via one API, so the team could test Anthropic, Mistral, Amazon models and pick the best tool for each task. They settled on Claude 3.5 Sonnet for classification and Nova Pro for text-to-SQL, balancing accuracy, latency and cost.

Because Bedrock is fully managed, the Innovation Lab avoided heavy infra work and moved from prototype to production in weeks. That’s a key takeaway: managed model services can compress delivery time for enterprise-grade AI.

How security and permissions remain central to usable enterprise AI

ClientIQ honours Salesforce’s permission model by mapping Okta group claims to metadata filters. When a user asks a question, their session carries division and region claims; queries are automatically constrained so only authorised documents are returned. In practice this means a user limited to EMEA never sees AMER notes, and admins can manage groups through an internal UI tied to Okta APIs.

This approach keeps governance tight without making the user experience clunky , answers still arrive naturally, they’re just scoped to what the user is allowed to see.

How they proved the system works: automated evaluations baked into CI/CD

TP ICAP built a measurement-led approach using Amazon Bedrock Evaluations. They created a 100-item ground truth set reflecting real questions, ran RAG evaluations to test different chunking, embedding models and FM choices, and used Bedrock’s evaluation reports to optimise retrieval precision and generation accuracy. Best bit: those tests run automatically in their CI/CD pipeline so every release is checked for regressions in quality.

This metric-driven loop isn’t glamorous, but it’s what makes the assistant reliable day-to-day and scales confident product iteration.

Practical tips if you want to copy their playbook

If you’re planning a similar project, start with clear user stories and a ground-truth test set. Chunk documents sensibly , one meeting per file worked for TP ICAP , and add metadata that reflects real filtering needs (region, division, date, author). Use hybrid search to reduce ambiguity and pick models per task to balance cost and latency. Finally, automate evaluation in CI/CD so quality stays high as you iterate.

And remember: traceability matters. Include source links in responses so consumers of the AI output can validate details quickly.

Closing line
Ready to make your CRM a searchable knowledge asset? Check Amazon Bedrock’s Knowledge Bases and Evaluations documentation and compare models to see which setup matches your data, privacy and cost needs.

Noah Fact Check Pro

The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.

Freshness check

Score:
10

Notes:
The narrative was published on 17 October 2025, making it highly fresh. No evidence of prior publication or recycling was found. The content appears original and not republished across low-quality sites or clickbait networks. The article is based on a press release, which typically warrants a high freshness score. No discrepancies in figures, dates, or quotes were identified. The content includes updated data and does not recycle older material.

Quotes check

Score:
10

Notes:
The article includes direct quotes from TP ICAP’s CEO, Nicolas Breteau, and AWS’s Tanuja Randery. These quotes are unique to this publication, with no earlier matches found online. The wording is consistent with the context and does not vary from other sources. No identical quotes appear in earlier material, indicating originality.

Source reliability

Score:
10

Notes:
The narrative originates from Amazon Web Services’ official blog, a reputable organisation known for its authoritative content. The article is co-written with Ross Ashworth at TP ICAP, a well-established financial markets infrastructure and data solutions provider. Both entities have a strong public presence and legitimate websites, enhancing the credibility of the report.

Plausability check

Score:
10

Notes:
The claims made in the narrative are plausible and align with known technological advancements in AI and CRM systems. The development of ClientIQ, an AI-powered assistant for CRM data, is consistent with industry trends. The narrative is covered by a reputable outlet, and the content includes specific factual anchors such as names, institutions, and dates. The language and tone are appropriate for the region and topic, with no inconsistencies noted. The structure is focused and relevant, without excessive or off-topic detail. The tone is professional and resembles typical corporate language.

Overall assessment

Verdict (FAIL, OPEN, PASS): PASS

Confidence (LOW, MEDIUM, HIGH): HIGH

Summary:
The narrative is fresh, original, and originates from reputable sources. The claims are plausible and well-supported by specific details. No significant credibility risks were identified, leading to a high confidence in the assessment.