Cresteo Success Cases

Real results, already shipped.

Two kinds of proof. Client delivery stories — measurable outcomes from real engagements across legal, insurance, aerospace, logistics, fintech and healthcare. And Forge AI agents — a growing library of agentic systems deployed on client infrastructure, each a teammate with a clear role and full human supervision.

01 — Client Delivery Stories

Measurable outcomes from real Cresteo engagements — software, AI, modernization and design, across industries.

24 case studies

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Legal

Client-Onboarding Automation for Legal

How Cresteo built custom automation to streamline client onboarding and communication for a U.S. legal firm. Read the case study.

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Legal

AI Legal Entity Extraction Case Study

How Cresteo built a governed AI/NLP pipeline that extracts 35 entities from legal documents at 90%+ accuracy - cutting manual review. Read more.

Read Case Study

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Aeronautic

PilotTrade: Flight-Swap Platform Build

Cresteo designed and launched PilotTrade, a pilot-centric flight-swap platform improving crew scheduling and work-life balance. Read more.

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Insurance

Legacy System to Cloud Modernization

Cresteo modernized a legacy client-server app - migrating to a scalable, cloud-based .NET architecture. Read the modernization case study.

Read Case Study

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Telecommunications

Agile Team Growth & Platform Evolution

Cresteo turned a fragmented engineering process into a modular, agile platform with standardized practices for versatility. See the case.

Read Case Study

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Food & beverage

Data Consolidation at Scale for FMCG

Cresteo built an ETL solution consolidating multiple SAP systems into a unified Azure repository for real-time analytics. See the case study.

Read Case Study

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Food & beverage

AI Order-Accuracy Detection for Kitchens

How Cresteo deployed real-time AI object detection in fast-food kitchens to catch order errors and lift accuracy. Read the case study.

Read Case Study

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Healthcare

AI Contaminant Detection for Medical Devices

Cresteo deployed real-time computer vision (YOLOv9) to detect contaminants in medical-device inspection - improving accuracy and assurance. Read more.

Read Case Study

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Healthcare

Right Team, Right Time: Breaking Bottlenecks

How Cresteo delivered the right technical team to break through delivery bottlenecks for a healthcare services provider. Read the case study.

Read Case Study

02 — Fintech Business Agents

Primary users are business areas (everyone except IT) and customers.

9 agents

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FINTECH · INTERNAL SUPPORT

Internal Process Guide

An always-on expert on your internal processes and systems — so staff stop escalating questions that already have answers.

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Internal Process Guide

FINTECH · INTERNAL SUPPORT

The challenge

Your staff — especially newer hires — keep escalating questions that don't need to be escalated, simply because they can't find answers that already exist. Give them an instant, always-on expert on your internal processes and systems, and those doubts get resolved on the spot.

How it works

A 24/7 conversational agent that acts as a master consultant on any internal process, product, or system, answering staff questions on its own.

Why it's safe to run

  • Draws answers straight from your knowledge base (Confluence, Notion, and the like), so they're always current
  • When something genuinely needs a human or falls outside its scope, it opens a pre-classified ticket (JIRA/ADO) with all the context already captured and routes it to the right person

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FINTECH · IT–BUSINESS BRIDGE

Steven — Business Relationship Management Agent

The business side's advocate at the CIO's table — turning hallway tech complaints into prioritized, well-specified requests that actually get built.

Read Case Study

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Steven — Business Relationship Management Agent

FINTECH · IT–BUSINESS BRIDGE

The challenge

Business teams always have technology needs that never make it to IT in any usable form — they get lost in hallway conversations or buried in the backlog. Steven is the business side's advocate at the CIO's table — capturing, shaping, and raising each department's needs until they become prioritized, well-specified improvements instead of forgotten asks.

How it works

A conversational agent in constant dialogue with your business stakeholders, proactively drawing out pain points, manual workarounds, and technology gaps. It:

  • turns what it hears into well-formed business cases, complete with the reasoning for how they're prioritized
  • routes them to the right IT or product owners
  • keeps a live register of every request and its status, and notifies stakeholders automatically — finally closing the loop between business and technology

Why it's safe to run

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FINTECH · BRANCH MANAGEMENT

Branch Manager Advisor

Every branch metric, live and in plain language, right inside WhatsApp — managers just ask, 'how are we tracking today?'

Read Case Study

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Branch Manager Advisor

FINTECH · BRANCH MANAGEMENT

The challenge

Branch managers make decisions all day, but the numbers they need are stuck in static reports or behind a slow request to the data team. Put the answers in their pocket — live, and right inside the channel they already use most.

How it works

A management advisor right inside WhatsApp that reports in real time on any operational metric or status across a branch. Managers just ask, in plain language — "how are we tracking today?" — and get a clear, structured answer backed by precise data from the bank's central systems.

Why it's safe to run

Growth vision — Global Banking Intelligence

From there it can reach beyond your own walls and fold in external data sources:

  • standing with the central bank and peer banks
  • local and regional financial-industry news
  • government moves that affect how the bank operates
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FINTECH · CREDIT & COLLECTIONS

Credit Compass

Turn collections from a dreaded call into a financial-advisory moment — reaching customers before arrears, with a plan they can actually manage.

Read Case Study

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Credit Compass

FINTECH · CREDIT & COLLECTIONS

The challenge

Most arrears don't happen because customers won't pay — they happen because nobody reached them at the right moment with the right information. Traditional collections is reactive, one-size-fits-all, and punitive. Turn that dreaded touchpoint into a financial-advisory moment:

  • catch early risk signals
  • reach the customer before they slip into arrears
  • guide them to a payment or refinancing path they can actually manage

How it works

A conversational agent that reads the customer's credit profile, payment history, and transactional behavior to know exactly when and how to step in. It offers payment options matched to what the customer can really afford — installments, refinancing, term extensions — and walks them through it step by step. The tone stays empathetic and solution-focused.

Why it's safe to run

  • Every option it offers stays within your policies
  • It brings in a human operator whenever needed, and makes no credit decisions on its own — it proposes, the customer accepts, and a person or authorized system confirms

Growth vision

The more it engages, the smarter it gets. Over time it can:

  • use predictive arrears-risk models to reach people before the warning signs are even obvious
  • tailor refinancing offers even more precisely
  • and — when you're ready — connect to your credit-origination system to adjust terms autonomously within approved limits
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FINTECH · CUSTOMER SUPPORT OPERATIONS

Customer Support Agent

Let an agent field the highest-volume support conversations — freeing your people for the complex, sensitive cases that actually need a human.

Read Case Study

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Customer Support Agent

FINTECH · CUSTOMER SUPPORT OPERATIONS

The challenge

Fintech support teams drown in repetitive, context-heavy questions — accounts, transaction disputes, product guidance — that still demand real knowledge of internal systems and regulatory limits. Let an agent take the highest-volume tier of those conversations, and your people get the room to handle the complex, sensitive cases that actually need a human.

How it works

A conversational agent trained on your product catalog, policies, current regulation, and transaction history that fields customer queries across chat and messaging channels. It:

  • answers questions about accounts and products
  • walks customers through dispute processes

Why it's safe to run

  • Hands off to a human — with full context — the moment things get complex or sentiment turns negative
  • Works directly with your banking core and CRM, and never exposes sensitive data outside your perimeter

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FINTECH · FINANCIAL CRIME

AML Investigation Officer

With AI-enabled fraud up 500% a year, this agent runs the whole investigation — evidence, narrative, SAR draft — and takes it from days to hours.

Read Case Study

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AML Investigation Officer

FINTECH · FINANCIAL CRIME

The challenge

Financial-crime investigation is reactive and slow — analysts piece together scattered signals by hand across systems that don't talk to each other. With AI-enabled fraud growing more than 500% year over year, no team can keep pace manually. Automate the entire investigation flow, and your analysts spend their time on judgment calls instead of data gathering.

How it works

An autonomous financial-crime investigation agent that:

  • spots suspicious patterns in transactional data
  • gathers and connects evidence across multiple data sources
  • writes structured investigation narratives
  • drafts Suspicious Activity Reports (SARs)

It takes investigations from days to hours, running around the clock and prioritizing cases by risk score.

Why it's safe to run

  • Every SAR comes ready for your analyst's review — the decision always stays with a person
  • Works right inside your existing case-management systems — nothing to rip out

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FINTECH / INSURTECH · CLAIMS OPERATIONS

Claims Document Assistant

Turn a pile of claim documents in every format into one structured, review-ready file — so your adjuster goes straight to the decision.

Read Case Study

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Claims Document Assistant

FINTECH / INSURTECH · CLAIMS OPERATIONS

The challenge

The moment a claim comes in, your adjuster is buried in documents in every format imaginable — photos, PDFs, medical records, police reports — and has to pull the relevant details out by hand before they can even assess coverage. It's the slowest, most repetitive stretch of the whole process, and exactly where claims stall. Hand your adjuster a file that's already structured, and they go straight to the decision.

How it works

An agent that:

  • takes claim documents in any format
  • pulls out and organizes the details that matter — parties involved, dates, amounts, what happened, the attached evidence
  • hands back a standardized file ready for review
  • checks for what's missing and asks the policyholder for it, conversationally

Why it's safe to run

  • Your adjuster gets a clean summary and makes the call on coverage and payment
  • The agent never changes a policy or kicks off a payment

Growth vision — Claims Intelligence Engine

As the agent proves itself on document extraction, it grows into autonomous handling of the full claims cycle:

  • coverage verification against policy terms
  • fraud-probability scoring
  • automatic reserve estimation
  • settlement calculation
  • and — when you're ready — payment initiation with human approval
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FINTECH · CFO / TREASURY

Treasury Reconciliation Assistant

Hand off the daily bank-vs-books grind — your treasurer sees only the discrepancies that need a decision, never the matching.

Read Case Study

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Treasury Reconciliation Assistant

FINTECH · CFO / TREASURY

The challenge

Every day, your treasury and finance team burns hours reconciling bank movements against the books:

  • downloading statements
  • cross-checking records by hand
  • chasing down discrepancies
  • documenting the result

It's repetitive, easy to get wrong, and adds nothing strategic. Hand it off, and your treasurer gets to focus on the exceptions and the decisions — not the matching.

How it works

A conversational agent that:

  • pulls bank statements and the accounting system via APIs
  • runs the reconciliation on its own
  • surfaces only the discrepancies that actually need your treasurer's attention
  • flags anything unusual, like duplicate payments or out-of-range amounts

Why it's safe to run

  • Delivers a fully traceable reconciliation report — every matched movement documented with its source and logic
  • Your treasurer reviews, approves, or escalates; the agent never changes a record or moves a payment

Growth vision — Treasury Command Center

Once autonomous reconciliation is running, the same system grows into an actively managed treasury:

  • cash-position forecasting from ERP and market data
  • catching synthetic invoices before funds ever move
  • liquidity optimization across accounts
  • and — when you're ready — autonomous execution of pre-approved payments within the limits your CFO sets
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FINTECH · COMPLIANCE / ANTI-MONEY LAUNDERING

AML Report Builder

Your compliance officer builds AML reports and drafts SARs in minutes, on demand — no more chasing the Data team, no more manual filing risk.

Read Case Study

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AML Report Builder

FINTECH · COMPLIANCE / ANTI-MONEY LAUNDERING

The challenge

Your compliance officer carries the full weight of Anti-Money-Laundering (AML) obligations — armed with internal docs, the regulator-approved protocol, and no real tooling. Every report means chasing the Data team or digging through systems by hand. It's slow, it makes compliance hostage to other people's availability, and every manual step is one more chance to file the wrong thing with the regulator.

So let's give your compliance officer full autonomy to:

  • build AML reports on demand
  • draft Suspicious Activity Reports (SARs) in minutes
  • keep an eye on the indicators that matter — without waiting on the Data team for a single query

How it works

A chat-based agent your compliance team simply talks to. It lets them:

  • ask questions of transactional data in plain language
  • get SAR drafts already in the regulator's required format
  • see the main AML indicators on one live dashboard

Why it's safe to run

  • Plugs into your existing systems through APIs, exposed over MCP (Model Context Protocol) — no rip-and-replace
  • Your officer stays in control: they review and approve every draft, and the agent never touches a record or makes a decision on its own
  • Its only job is to take the friction out of collecting, structuring, and presenting information
  • Audit-ready by design — every source consulted, every logic step, and the full version history of each report is logged

Growth vision — Compliance Autopilot

Start where the risk is lowest, then scale as confidence grows. The same foundation extends into fully autonomous compliance orchestration:

  • new reports and data sources
  • continuous transaction monitoring with automatic alerts
  • KYC screening at onboarding
  • autonomous investigation of suspicious activity
  • real-time reading of regulatory changes

Every phase delivers value before the next one starts — and none of it disrupts the way your team already works.

03 — IT Agents

Primary users are teams in the IT area.

5 agents

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IT · PRODUCTION MONITORING

Argos + Iris — Production Support Agents

Catch production issues the moment they happen, diagnose them fast, and trace every one to the exact requirement and PR behind it.

Read Case Study

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Argos + Iris — Production Support Agents

IT · PRODUCTION MONITORING

The challenge

Production problems get caught late, diagnosed slowly, and traced back to the requirement and PR that caused them only after hours of manual digging — your on-call engineers stitching together logs, metrics, and code history at the worst possible time. Make detection continuous, diagnosis fast, and root-cause tracing automatic.

How it works

  • Argos (Detection) watches production around the clock, catching anomalies, threshold breaches, and error spikes, and bundling the related signals into clean, severity-rated incident packages.
  • Iris (Diagnostic) picks up that package, pulls in whatever extra data it needs, and writes a clear diagnostic that names the exact requirement and PR behind the problem.

Your Tech Leader reviews, confirms or redirects, and Bugsy files a fully linked bug ticket — origin to fix, all connected.

Why it's safe to run

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IT · DATA ENGINEERING

Database Migrations Agent

Make your riskiest work — schema migrations — systematic, validated, and documented, and take the danger off your senior engineers.

Read Case Study

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Database Migrations Agent

IT · DATA ENGINEERING

The challenge

Schema migrations are some of the riskiest work your team touches — high stakes, easy to break, and dependent on deep knowledge of both the data model and everything downstream. So they get postponed, or rushed by hand under deadline pressure, and that's how production incidents happen. Make every migration systematic, validated, and documented — and take the risk (and the planning burden) off your senior engineers.

How it works

An agent that:

  • reads your existing schema and where you want it to go
  • generates the migration scripts, rollback strategies included
  • flags breaking changes and downstream dependencies before they bite
  • lays out a structured execution plan with a clear risk assessment

Why it's safe to run

  • Tests every script against staging data before promotion
  • Produces the audit documentation for you
  • Raises a flag the instant data integrity is at risk
  • Wired into your existing CI/CD pipeline and version control

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IT · REQUIREMENTS REVIEW

Reid — BA Quality Gate Agent

The quality gate that reviews every ticket before it reaches developers — catching ambiguities and missing edge cases while they're still cheap to fix.

Read Case Study

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Reid — BA Quality Gate Agent

IT · REQUIREMENTS REVIEW

The challenge

A flaw caught at the requirements stage costs a fraction of the same flaw caught in development — 5–10× less. Yet tickets reach developers every day without a proper check for completeness, clarity, or business-rule compliance. Reid is the permanent quality gate that reviews every ticket before it hits the backlog, catching ambiguities, missing edge cases, and rule violations while they're still cheap to fix.

How it works

A reviewer agent that runs a structured BA quality checklist over every ticket:

  • clear acceptance criteria
  • INVEST criteria (Independent, Negotiable, Valuable, Estimable, Small, Testable)
  • business-rule coverage
  • defined scope
  • and no ambiguities

It leaves specific, actionable comments — never vague flags — and sends tickets back to Vera with exactly what to fix. And it keeps a quality log, so the patterns behind your incomplete requirements stop being a mystery.

Why it's safe to run

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IT · REQUIREMENTS

Vera — BA Requirements Agent

A permanent, always-available Senior BA that turns out complete, correctly structured requirement tickets from day one.

Read Case Study

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Vera — BA Requirements Agent

IT · REQUIREMENTS

The challenge

Bad requirements are the single biggest source of rework in software projects — and good ones eat huge amounts of your BAs' time in elicitation calls, even though most of the work follows clear rules. Vera is a permanent, always-available Senior BA who turns out complete, correctly structured requirement tickets from day one.

How it works

A conversational BA agent that runs structured elicitation sessions with your Business Owners, asking the right questions to pin down scope, business rules, edge cases, and acceptance criteria. It:

  • writes every ticket a feature needs — user stories, tasks, and sub-tasks — formatted to your own JIRA conventions
  • sends them to the reviewer agents (Reid, Artemis, Daphne, Quinn) in parallel and folds in each one's feedback

Why it's safe to run

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IT · SOFTWARE DELIVERY

Cresteo Forge — Full Agentic SDLC

A permanent team of 10 AI agents across your entire software lifecycle — so your engineers move up from mechanical first-pass work to judgment and approval.

Read Case Study

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Cresteo Forge — Full Agentic SDLC

IT · SOFTWARE DELIVERY

The challenge

So much of software delivery still rides on people doing high-volume, knowledge-heavy work by hand:

  • writing requirements
  • reviewing code
  • creating test cases
  • monitoring production

It's slow, inconsistent, and bottlenecks every phase. Forge drops a permanent team of AI agents across your entire Software Development Life Cycle (SDLC) — so your people stop doing the first-pass mechanical work and move up to judgment, supervision, and approval.

How it works

A 10-agent team, organized into four modules that mirror how you already build:

  • Context Ingestion: every agent learns your organization inside-out before it talks to a single person.
  • Module 1 — Requirements: Vera elicits needs and writes the tickets; Reid checks scope, rules and completeness; Artemis validates feasibility, security and performance; Daphne validates data-model impacts; Quinn sets acceptance criteria and builds test plans.
  • Module 2 — Production: Enzo builds the components and opens PRs; Desmond reviews the UI; Artemis & Daphne review code and data; your human validator approves and merges.
  • Module 3 — Support: Argos watches for anomalies; Iris diagnoses and traces root cause; Bugsy files the linked bug tickets.

Why it's safe to run

04 — Other Industry Sectors

Agents serving any area in industries outside fintech.

4 agents

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HEALTHCARE · CLINICAL OPERATIONS

Clinic Analysis Reviewer

Give every lab analysis a protocol-aligned first read — so your physicians open structured interpretation, not a wall of raw numbers.

Read Case Study

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Clinic Analysis Reviewer

HEALTHCARE · CLINICAL OPERATIONS

The challenge

Lab results don't mean much until someone reads them against the patient's history, the reference ranges, and clinical protocols — and doing that by hand creates delays and inconsistency in how results get prioritized and communicated. Give every analysis a consistent, protocol-aligned first read, so your physicians open structured interpretation, not a wall of raw numbers.

How it works

An agent that:

  • takes in lab results
  • weighs them against the patient's history and established clinical reference ranges
  • flags out-of-range values and rates how serious they are
  • writes structured interpretation notes aligned to your clinical protocols

It surfaces the patterns that need urgent attention.

Why it's safe to run

  • Formats everything to drop straight into your existing medical-record system
  • The physician's review and sign-off is always the final word

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LEGAL / JUSTICE · CASE MANAGEMENT

Police Report Processor

Make report intake systematic, consistent, and fast — so investigators get complete, correctly classified cases without the administrative wait.

Read Case Study

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Police Report Processor

LEGAL / JUSTICE · CASE MANAGEMENT

The challenge

Report intake is slow, inconsistent, and only as fast as whoever's on shift. Reports show up in every format and every state of completeness, and someone has to triage, classify, and route each one by hand before an investigation can even start. Make intake systematic, consistent, and fast — so investigators get complete, correctly classified cases without the administrative wait.

How it works

An agent that:

  • takes reports in any format — document upload, structured form, or conversational intake
  • pulls out and organizes the key details (type of event, parties, location, timeline, evidence)
  • classifies each report against the relevant legal taxonomy
  • checks for what's missing and asks for it when needed
  • routes the finished case package straight to the right investigative unit

And it keeps a complete audit trail of the whole intake, start to finish.

Why it's safe to run

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e-LEARNING · STUDENT EXPERIENCE

Recommendations Engine

Make every student's path feel hand-built for them — lifting engagement and completion with zero manual curation.

Read Case Study

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Recommendations Engine

e-LEARNING · STUDENT EXPERIENCE

The challenge

A generic catalog gives everyone the same generic journey. Without personalization, students can't find what's relevant, completion rates slide, and your platform's value quietly erodes. Make every student's path feel hand-built for them — lifting engagement, completion, and satisfaction, with zero manual curation.

How it works

A recommendations agent that reads how each student actually behaves (what they've finished, how they're scoring, where they engage, what they're aiming for) alongside organizational context (role, area, skill gaps) to surface the right courses and content for each person. It keeps adjusting as behavior shifts, plugs into your LMS, and shows up right where students already are — including their messaging channels.

Why it's safe to run

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e-LEARNING · DATA & REPORTING

Rex — Reporting Agent

Business reporting that's autonomous, on time, and open to anyone — just ask in plain language, no SQL required.

Read Case Study

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Rex — Reporting Agent

e-LEARNING · DATA & REPORTING

The challenge

At e-learning companies, the same business questions come up again and again — and answering them means pulling and stitching together data from the LMS, the CRM, payment systems, then making sense of it. It's repetitive, technical, and pulls people away from the very decisions the reports are supposed to inform. Rex makes reporting autonomous, on-time, and open to anyone — no SQL required.

How it works

A reporting agent wired into your data sources that:

  • answers business questions in plain language
  • builds structured reports the moment you ask
  • delivers your scheduled reports to the right people, automatically

It keeps an eye on the metrics that matter — enrollment, completion, revenue per course, engagement — and pings you the moment one drifts out of range. Every output is built for humans to read, with the backing data and sources right there.

Why it's safe to run

Want an agent like these on your team?

Every one of these was built on Cresteo Forge — deployed on the client's own infrastructure, with full human supervision. Let's design yours.