Case studies

Systems at work — across a dozen industries

40+ delivered systems: from document intelligence inside a hospital, through finance back offices, to mobile platforms. Below, selected deliveries — described the way our line of specialized agents built them.

🏥Healthcare prototype in 3 weeks

Document intelligence for a hospital — entirely inside the walls

The challenge

A regional hospital: thousands of pages of procedures, standards and medical documentation, scattered across folders and versions. Staff lose time searching, and regulations rule out sending anything to the cloud.

What we built

A document-intelligence system running entirely on the hospital's own hardware, without a single outbound request: topic-scoped knowledge libraries, answers with citations pointing to document and page, and compliance validation of new documents against the standards in force.

How the line built it

The agents built the indexing pipeline and the answer layer in an isolated environment mirroring the target hardware. QA fired thousands of control questions at the system and compared citations against sources — proof by proof, before the system ever saw real data.

How the system works

Documents →Indexing →Topic libraries →Cited answers →Compliance check

A document's journey through the system — all on the client's hardware

Outcomes

0 documents ever leave the building
seconds to a cited answer instead of hours of searching
3 wks from signature to a working system
🎧Operations / customer service prototype in 2 weeks

A customer assistant wired to a real backend

The challenge

A subscription platform with a growing user base: the same questions about accounts, balances and billing flooded the team, and every answer meant checking three systems at once.

What we built

An AI assistant integrated with licensing, invoicing and referrals: it performs account operations itself — registration, balance checks, service actions — and answers questions with citations from the knowledge base. Humans get only the unusual cases.

How the line built it

The line got access to the client's API test environment. Agents built the integrations in parallel with the conversation layer, while QA agents played out real user scenarios against the running system — every flow recorded as evidence.

How the system works

Customer question →Intent →Backend →Cited answer →Escalation

One conversation, four integrated systems

Outcomes

~80% of requests handled with no human involved
4 systems integrated behind one assistant
24/7 availability with no queue
💳Finance prototype in 2 weeks

A finance back office that closes the month by itself

The challenge

A services company: hundreds of incoming invoices a month, manual matching against payments, reminders sent "when someone remembers", and a month-end close that cost several days of work every month.

What we built

A finance back-office automation: incoming invoices read and matched to payments, reminders sent on schedule, expenses categorized for accounting — and a month-end checklist that fills itself in and shows what genuinely needs a human.

How the line built it

The agents reconstructed the finance process from the client's documents into a signed specification, then built the automations one by one in an isolated environment on synthetic data. QA checked the matching against thousands of edge cases — from duplicates to partial payments.

How the system works

Invoice arrives →Extraction →Matching →Categorization →Month-end close

From an inbox full of invoices to a closed month

Outcomes

~90% of invoices matched automatically
days → hours what the month-end close takes now
0 forgotten payment reminders
🎙️Voice / meetings prototype in 2 weeks

Conversation intelligence: every meeting leaves a trace

The challenge

Sales and management spent dozens of hours a week in conversations that left nothing behind but hasty notes. Decisions got lost, the CRM was permanently out of date, and the knowledge inside those calls was unavailable to the rest of the company.

What we built

A conversation-intelligence system: meeting recordings transcribed and summarized automatically, decisions turned into tasks and CRM entries, and everything landing in a searchable archive where every answer points to the recording and the exact moment.

How the line built it

The agents built the transcription pipeline and the extraction layer in parallel, on test recordings of varying quality. QA compared extracted tasks against manually written minutes until discrepancies fell below the threshold set in the acceptance criteria.

How the system works

Recording →Transcription →Decisions →Tasks & CRM →Archive

From meeting to tasks — without taking notes

Outcomes

100% of meetings get an automatic note and tasks
hours/week recovered from note-taking and CRM upkeep
1 question between you and any past decision
📱Care / mobile prototype in 3 weeks

A mobile support platform with an AI mediator

The challenge

An organization supporting people in crisis: caregivers aren't available around the clock, and people reach out exactly when support is hardest to find. An ordinary chatbot was out of the question — the safety of the person talking is at stake.

What we built

A mobile app where an intelligent AI mediator carries the conversation between a person seeking help and their caregiver: it keeps contact going, organizes the threads and passes the caregiver what matters — with built-in safety guardrails and hard escalation rules — fixed conditions that tell the AI when it must hand the conversation to a human (e.g. on risk signals).

How the line built it

The safety guardrails were built first — as signed acceptance criteria — and they took the heaviest fire from the QA agents, across thousands of difficult-conversation scenarios. Only behind that safety gate did the line build the app and the caregiver panel.

How the system works

A person writes →AI mediator →Risk signals →Caregiver

The mediator in the middle — a human always in reach

Outcomes

24/7 contact, even while the caregiver sleeps
100% of risk signals escalated to a human
3 wks from signature to a prototype on phones
📊Data / reporting prototype in 3 weeks

A live operations dashboard instead of the Friday spreadsheet

The challenge

The board of a services group steered the company on reports glued together by hand once a week: data from several systems, conflicting versions, and anomalies noticed a week late.

What we built

A live operations dashboard: KPIs streamed from source systems in real time, anomaly alerts before problems grow, and management reports that write themselves — with numbers that always agree, because they have one source.

How the line built it

The agents first mapped the source systems and reconciled the metric definitions — as a document signed by the board. Then the data pipelines, the alert layer and the report generator were built in parallel, with QA comparing dashboard output against historical manual reports.

How the system works

Source systems →Data stream →Metrics model →Dashboard + alerts →Reports

One source of truth, from stream to report

Outcomes

a week → minutes management data latency
1 source of truth instead of conflicting versions
0 h of manual report-gluing every week

Also from the same line

🗂️

Back office

Automated document intake: invoices, contracts and applications read, validated and routed into your systems — a human checks only the uncertain cases.

📚

Knowledge

A company knowledge engine with cited answers, built on internal documents and procedures — also fully on-premise.

📈

Sales

Automated customer follow-through: lead scoring, replies in the company's tone, an always-current CRM, handover to a human at the moment of intent.

🧑‍💼

Hiring / HR

Candidate screening: applications read and scored against role criteria, summaries and a shortlist with verifiable reasons.

💬

Communities

Telegram/Discord community operations: onboarding, moderation, announcements and engagement analytics — one AI assistant.

Your industry could be next. The line isn't tied to any of these domains — it's the same agents, pointed at a different problem. A conversation about your case costs 10 minutes.

40+ systems delivered
12+ industries served
24/7 the line never stops
100% deliveries at a fixed price and date

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