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B2B Platforms

B2B Platform AI Feature Integration

An AI capability layer for a live B2B platform with tenant-aware retrieval, assisted workflows, release gates, and product telemetry.

Explore How We Engage See What We Build
Challenge Context and constraints made explicit
Approach Architecture choices connected to tradeoffs
Outcome Operational gains framed in practical terms
Learning Patterns reusable across future initiatives

Enterprise context

A B2B platform team wanted to introduce AI-assisted search, account summarization, and workflow recommendations for thousands of business users. The platform already had tenant isolation, strict latency expectations, and customer-facing SLAs, so the AI work had to behave like a product capability rather than an experimental prompt wrapper.

Challenge

The product team needed natural-language search, assisted workflow recommendations, and in-product summarization while preserving tenant isolation, latency targets, cost visibility, and release confidence.

Approach

ViaCatalyst designed the AI feature layer around retrieval boundaries, model routing, evaluation datasets, observability events, and guarded workflow actions that fit the existing product architecture.

Impact snapshot

Representative enterprise impact indicators.

The metrics are framed as anonymized program indicators and delivery targets from the case pattern, useful for understanding the scale of improvement the architecture is designed to unlock.

Assisted task completion +34%

Beta users completed configured research and summary workflows faster with AI assistance enabled.

P95 AI latency 2.7 sec

Model routing and context compression kept the primary answer path under the product latency budget.

Tenant isolation tests 100%

Cross-tenant negative tests passed before expanding beta access.

Cost visibility 94%

Tracked AI runs captured task, model, token, latency, and user-action metadata for cost attribution.

Workflow completion rate

Higher is better

Before
58%
After
78%

Average summary preparation time

Lower is better

Before
22 min
After
8 min

Tracked AI runs

Higher is better

Before
24%
After
94%

Architecture

How the enterprise AI system is structured.

Each case pattern is framed around data boundaries, workflow controls, validation, and operating visibility.

AI feature boundary

The AI layer is treated as a product capability with contracts for data access, prompts, model routing, and user-facing behavior.

  • Tenant-aware context assembly
  • Model routing by task risk and latency
  • Feature flags for staged release

Product telemetry

Events connect user intent, retrieval quality, answer acceptance, correction behavior, and cost drivers.

  • Topic and feature-intent events
  • Prompt, retrieval, latency, and token traces
  • Feedback loops for corrections and escalations

Release gates

Quality thresholds are defined before rollout so product teams can ship AI changes with measurable confidence.

  • Task-success rubrics
  • Tenant-isolation tests
  • Cost and latency baselines

Implementation focus

What the work clarifies.

  • Mapped high-value product moments where AI could assist without taking unsafe action.
  • Separated read-only answer generation from guarded workflow actions and write paths.
  • Designed prompt, retrieval, and model-version tracking so product quality could be reviewed after release.
  • Defined rollout stages for internal testing, limited beta, monitored release, and production operation.

Enterprise impact

Why the pattern matters.

  • Turned AI from a sidecar prompt feature into a governed product capability.
  • Gave product, engineering, and customer success teams shared visibility into quality and adoption.
  • Reduced release risk by connecting tenant isolation, evaluation, and telemetry before launch.

Next step

Turn a similar challenge into a roadmap.

Start with the Two-Week Architecture Audit so data access, workflow risk, validation, and operating needs are clear before build work expands.

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