Signal to system - case studies

The business value comes first.
The solution comes second.

Four cases showing how Jutro starts with business value, chooses the simplest credible system and tests it against the current way of working.

01 Understand the real problem
02 Choose the simplest credible move
03 Test against the baseline
04 Scale, revise or stop

Case index

Four ways AI becomes useful only after the problem is understood.

Healthcare evidence navigation, operations workflow, synthetic customer research and traffic forecasting. Different domains, same discipline: human accountability, baselines, provenance, observability and explicit stop conditions.

A deployed evidence navigator for oncology reviewers, with source-linked answers, visible uncertainty and explicit quality boundaries.

Controlled evidence sourcesSemantic retrievalDeterministic guardrailsLLM-assisted synthesisReviewer validation

Problem

Reviewers had to synthesise fast-moving NSCLC evidence across scattered sources under clinical review pressure.

System

A bounded navigator combined controlled sources, semantic retrieval, deterministic guardrails and LLM-assisted synthesis.

Evaluation

Retrieval correctness and citation faithfulness.
Answer faithfulness and time-to-evidence.
Reviewer agreement and error analysis.

Boundary

Every answer had to remain traceable to evidence and review. Unverifiable citations, hallucinations or low reviewer trust would stop or redesign the system.

Before

Reviewers manually stitched together fast-moving oncology evidence from scattered sources, with limited time to inspect how a summary was produced.

After

Reviewers can navigate source-linked evidence, visible uncertainty and a traceable review trail. Clinical judgment stays with the reviewer.

A deterministic contract-to-payout lifecycle designed to replace fragmented handoffs and approximately 30% outsourced processing cost.

IntakeVendor dataApproved templatesSignature trailPayout readiness

Problem

Email, spreadsheets and disconnected tools obscured ownership and status, while outsourcing added approximately 30% to processing cost.

System

One visible lifecycle joined intake, approved templates, verification, signatures, accounting handoff and payout readiness.

Result

The organisation gained an internal process with visible ownership and human review at consequential steps.

Boundary

Approved legal templates remain the source of truth. The system must preserve status and decision history without increasing legal risk.

Before

Contract-to-payout work was split across email, spreadsheets and disconnected tools. Ownership and status disappeared between handoffs, while outsourcing added approximately 30% to processing cost.

After

One internal lifecycle now connects approved templates, signatures and payout readiness, with visible ownership and human review at consequential steps.

An AI-assisted screen for removing weaker concepts before expensive human-panel research.

Concept inputsAudience segmentsSynthetic responsesSemantic Similarity RatingPanel candidates

Problem

Weak concepts could consume panel budget before the client had a useful signal about their potential.

System

A local pilot generated segment-specific qualitative responses and comparable scores, then passed the strongest candidates to real validation.

Evaluation

The screen had to produce stable results and send better candidates into paid research than intuition alone.

Boundary

Human panels remain the benchmark. The synthetic layer is only preselection and stops if its results are unstable or fail to correlate with panel outcomes.

Before

Early concepts reached expensive panel research too soon or were filtered by intuition alone.

After

Teams can screen out weaker concepts before taking the strongest candidates to human-panel validation. Real respondents remain the benchmark.

An in-progress forecasting MVP for better staffing decisions, with manager approval kept in the loop.

Historical trafficWeather + local eventsExpected demandStaffing recommendationManager approval

Problem

Managers planned staffing reactively from calendars, experience and incomplete demand signals.

System

The MVP combines historical traffic, weather and local events to forecast demand and recommend staffing for manager review.

Evaluation

Retrospective tests must beat simple day-of-week and manager-experience baselines before a live pilot.

Boundary

Managers keep the decision. The work stops if data is too incomplete, the model misses the baseline or recommendations are not useful.

Before

Managers plan staffing reactively from experience, calendars and incomplete demand signals.

After

The proposed MVP would let managers review an evidence-based demand forecast and adjust the recommendation before scheduling. Managers retain the decision.

Working on a business problem that might need AI?

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The business value comes first. The solution comes second.

Working on a business problem that might need AI?

The business value comes first.
The solution comes second.