• USE CASES – WORKED EXAMPLES

Patterns we've seen actually move the numbers.

Composite portraits of the kind of work Ucentix does: the situation, what we built, and what changed. Names omitted; shapes are real.

CASE 01 – FINANCE

01 Relationship intelligence that teams actually trusted.

An enterprise sales team needed to reveal warm paths through complex accounts without asking reps to dig through CRM notes, calendars, and team memory by hand.

BEFORE

Signal trapped in the system.

Useful relationship clues existed, but they lived across email, CRM notes, meeting metadata, and private team memory.

– Search relied on memory
– Data scattered across tools
– High-value intros too slow

SOLUTION

A relationship layer with a UI worth opening.

We connected first-party signals to a graph database, layered in LLM enrichment, and shipped a search surface designed around the question: who is closest to this person?

– Metadata-only pipeline
– LLM enrichment with citations
– Search-first workflow

AFTER

Warm intros, on demand.

Bankers get ranked internal connectors, shared history, last-touch dates, and a one-tap intro request in minutes instead of days.

– Hours saved per outreach
– Higher intro reply rates
– Used in 80%+ of pursuits

-92%

Time to identify the warmest internal path to a target executive.

3.4x

Increase in successful intros within the first quarter after rollout.

87%

Weekly active usage among the deal-team population by week eight.

0

Privacy escalations, with the pipeline operating on metadata only.

CASE 02 – PRODUCT OPS

02 A Slack-native AI bot that turned product conversations into tickets.

Product managers, designers, and engineers were already discussing roadmaps, requirements, and bugs in Slack. Lexa met them there: turning rough thread context into structured tickets without forcing the team into another workflow.

BEFORE

Good context, slow capture.

Roadmap ideas, design requirements, dev tasks, and bug reports were scattered across Slack threads. The context was rich, but turning it into actionable tickets still took manual cleanup and follow-through.

– Requirements lived in conversations
– Bug reports lost momentum
– Ticket creation interrupted the team

SOLUTION

Lexa created the ticket from the thread.

We embedded an AI bot directly into Slack. A teammate could reply in a thread, ask Lexa to create a roadmap item, design requirement, dev ticket, or bug, and the bot would structure the ticket, assign it, and preserve the original context.

– Slack-native ticket creation
– Roadmap, design, dev, and bug workflows
– Context preserved from the thread

AFTER

Tickets appeared while the context was still fresh.

Product and engineering teams could capture work at the moment it surfaced. PMs wrote stronger tickets faster, and bug reports moved from Slack thread to assigned work item without the usual copy-paste drag.

– Faster product-ticket drafting
– Bug tickets created from replies
– Less handoff between Slack and delivery tools

1 reply

Enough for Lexa to draft a usable ticket from the Slack thread where the work emerged.

4 flows

Roadmap, design requirements, dev tasks, and bug reports handled through the same Slack-native interaction.

<5 min

Ticket creation moved from a separate cleanup task to a quick action while the details were still clear.

0 copy-paste

Original thread context stayed attached to the work, reducing ambiguity for PMs, designers, and engineers.

CASE 03 – ENGINEERING QA

03 Autonomous tests generated from real Jam bug reports.

Engineering needed a better way to turn real user-reported failures into repeatable rollout checks. Jam videos already captured the behavior, but converting them into scripts took time and often happened after the next release risk had already arrived.

BEFORE

Bug evidence was trapped in recordings.

Jam videos showed exactly what broke, but engineers still had to interpret the steps, recreate the scenario, and manually decide what should become an automated regression test.

– Reproduction steps buried in video
– Manual script creation
– Regression checks lagged behind fixes

SOLUTION

AI converted Jam context into runnable tests.

We integrated an autonomous testing workflow that could inspect Jam videos, infer the user path, create a test script, and run it after rollouts so recurring failures were checked without waiting for a manual QA pass.

– Jam video understanding
– Script generation from observed behavior
– Rollout-triggered autonomous test runs

AFTER

Real bugs became repeatable rollout checks.

The team could close the loop from reported bug to regression coverage. Each rollout had a stronger safety net because tests were based on the behavior users actually experienced, not only the paths engineers remembered to script.

– Faster regression coverage
– Tests tied to real user evidence
– More confidence after each rollout

1 Jam

A bug recording became enough source material for a repeatable automated check.

1 rollout

Generated scripts could run after each release to catch repeated failures before users did.

-QA lift

Engineers spent less time translating videos into scripts and more time fixing the underlying issue.

Real paths

Coverage was grounded in actual reported behavior rather than only ideal happy-path testing.

See your workflow somewhere in there? Let's talk.

Ucentix.

THE STUDIO

AI consulting built on 14 years of product and UX design.

PRACTICE

Strategy
Implementation
UX integration

COMPANY

About
Usecase
Contact

REACH US

hello@ucentix.com
Book a consultation