<2 sec
Time-to-insight,
mid-conversation
30–50%
Less pre-meeting
prep time
4 models
Specialist LLMs
cross-validating advice
Duty
Suitability record
per recommendation

Time-poor RMs, uneven advice, scattered evidence

Advice quality varied across the network and suitability rationale was hard to reconstruct. Three gaps compounded — before an RM even opened a client conversation.

Fragmented client context

RMs pieced together client data from CRM, portfolio, product systems, and free-text notes before every meeting — roughly 40% of their time spent preparing across 4–6 systems rather than advising.

Uneven advice quality

Recommendations varied by RM tenure and branch. Top performers' reasoning was never systematised, so a client's outcome depended in part on who they happened to sit across from.

Suitability evidence gaps

Suitability rationale lived in free-text notes, making a defensible record hard to reconstruct for Consumer Duty reviews — and life-event and product-fit signals often surfaced too late to act on.

A specialist model ensemble, with the RM in the loop

Not one model producing advice — several specialist models that assemble context, cross-check each other, and hand the RM a reviewed, traceable recommendation.

Specialist model ensemble

Distinct Bedrock-hosted models for market view, portfolio context, product suitability, and compliance validation — routed by a lightweight orchestrator on a sub-second real-time path, so the RM gets a pre-assembled brief mid-conversation.

Disagreement as a signal

An async path runs the specialists in parallel and surfaces where they disagree to the RM, rather than silently resolving it. Cross-validation across models becomes a suitability control, and single-model concentration risk is removed.

Consumer Duty-ready audit

Every model input, output, and RM action is written to immutable S3 with Object Lock and full CloudTrail — a defensible suitability record per recommendation, ready for Consumer Duty and SM&CR review.

End-to-end flow
Context Route Specialist LLMs · parallel Synthesise / Detect Disagreement RM Review Recommend Record · Suitability + Audit
Built on AWS Bedrock — four specialist LLMs via PrivateLink, Step Functions orchestration, a Lambda router / judge, a SageMaker disagreement model, DynamoDB & Aurora context stores, and WORM S3 audit — deployed multi-region (London primary, Ireland warm standby) with client data pinned in-region and no internet egress for model traffic.

Faster, more consistent advice — with a defensible record

Outcomes the platform was designed to deliver for the bank's leadership. Figures below reflect program targets.

Speed
<2 sec
Real-time context surfaced mid-conversation, not assembled beforehand.
Productivity
30–50%
Less pre-meeting prep as specialist models assemble the client brief.
Consistency
One voice
Top-performer reasoning systematised into models across every branch.
Compliance
Duty
A traceable, on-demand suitability record for each recommendation.
Resilience
4 models
Cross-validation replaces single-model concentration risk.
Residency
In-region
Client data never leaves region; model traffic stays on private networking.

RM-in-the-loop, not replaced

The co-pilot assembles and cross-checks; the relationship manager reviews, decides, and owns the recommendation at the point of conversation.

Disagreement as a control

Surfacing where models disagree turns multi-LLM redundancy into an active suitability safeguard rather than noise to be hidden.

Consumer Duty defensibility

Every recommendation traces to the models and evidence that produced it — a defensible record ready for Consumer Duty and SM&CR review.

Our RMs walk into every conversation already briefed, our advice is consistent across the network, and we can show a regulator exactly how each recommendation was reached.
Program outcome · Large regional bank engagement

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