Use cases
From process
to working system.
Eight engagements of the kind we deliver — from sales, marketing and reporting to finance and internal knowledge. Each case shows the situation, what we built and the effect it had.
Discuss your AI projectREPRESENTATIVE CASES. THE EFFECTS ARE TYPICAL FOR THE INTRODUCTION, NOT PROMISES.
CASE / Sales
The sales agent that did the research
A B2B company with ten sellers spent four hours per person every week finding material and updating the CRM.
Related solutionThe situation
The team knew the customers but not the time. Research before meetings, lead summaries and CRM updates took four hours per seller and week — hours taken from qualified conversations.
What we built
- An agent that searches approved sources and assembles an account brief before meetings
- A structured lead profile with needs, roles and next steps
- CRM updates with human approval before any outbound action
- A weekly report on pipeline and data quality
The result
Four hours per seller and week returned to customer conversations. Follow-up became more consistent because the next step was documented in the CRM.
CASE / Support
Support that knew the contracts by heart
A support team of 25 answered questions about contracts, terms and routines from twelve different systems.
Related solutionThe situation
The answers existed, scattered across contracts, policies and three older ticketing systems. New hires needed months to become self-sufficient, and customers got different answers depending on who replied.
What we built
- A permission-aware assistant across contracts, policies and tickets
- Answers with source references and an escalation path
- Synchronisation with the existing ticketing queue
- An evaluation set built from real customer questions before launch
The result
Common questions are now answered in under a minute with a traceable source. Escalations dropped, and new hires handled qualified cases after two weeks instead of two months.
CASE / Finance
The invoice flow that reviewed itself
A company with 40,000 incoming documents a year handled invoices manually in three steps.
Related solutionThe situation
Invoices and delivery notes arrived as PDFs, scans and email in varying formats. Three people registered and approved them by hand, with a queue at month end and little visibility into exceptions.
What we built
- Classification and extraction of agreed fields
- Validation against contracts, amount limits and supplier records
- Automatic matching against purchase orders
- A review queue for uncertain items with an audit trail
The result
Eighty percent of documents are now handled without manual registration. Lead time from received invoice to approval went from three days to four hours, and exceptions are visible in their own interface.
CASE / Insight
The insight platform that answered questions
A management team wanted to follow margins per customer and segment without waiting for report requests.
Related solutionThe situation
Data lived in ERP, CRM and three spreadsheets owned by different people. Every question required a request, and answers were based on different definitions of the same metric.
What we built
- A shared data model with common metric definitions
- Dashboards for margin, volume and deviation
- Natural-language questions against approved sources
- Row-level permissions tied to existing roles
The result
The management team follows development and asks follow-up questions instead of ordering reports. Time from question to answer went from two days to fifteen minutes, with one definition for everyone.
CASE / Internal systems
The knowledge assistant for operations
A technically heavy company lost knowledge every time an experienced engineer left.
Related solutionThe situation
Runbooks, tickets and internal guides lived in five systems and two wikis. A handful of key people became bottlenecks because only they knew where the answers were.
What we built
- An assistant across runbooks, tickets and guides
- Role-based access per team and system
- Links between alerts, tickets and resolution history
- A feedback loop where engineers correct answers directly
The result
New engineers resolve common alerts without calling a colleague, and knowledge stays in the system when people change roles. Onboarding dropped from six weeks to three.
CASE / Proposals
The proposal assistant that gave the team a day back
A company producing ten proposals a month spent three days per proposal on research and a first draft.
Related solutionThe situation
Every proposal required reviewing past deliveries, the requirements specification and the pricing model. The knowledge sat with two people, and quality varied with the time available.
What we built
- A multi-agent flow for research, drafting and quality review
- Templates and previous proposals as a knowledge base
- Checkpoints where the bid owner approves
- Traceability per section and source
The result
A first draft now takes a day instead of three, with more consistent quality and explicit review points. The bid team spends its time on price and solution instead of hunting for material.
CASE / Marketing
The marketing agents that replaced an agency
A company with an in-house marketing team paid an agency every month and still received reports two weeks after month end.
Related solutionThe situation
SEO, advertising and reporting were handled by an external agency. Every request took days, the agency owned the data and each new campaign needed a new brief. The fee was fixed regardless of results.
What we built
- Agents for keyword and topic research with volume, intent and competition
- Automatic content briefs and metadata for publishable pages
- Ad variants, audience segments and budget control per channel
- A weekly ROI and ROAS report with recommended actions
The result
The team now produces briefs and ad variants itself instead of ordering them. ROAS increased, cost per conversion fell and marketing spend ended up below the agency retainer.
CASE / Reporting
Reporting that went from spreadsheets to answers
A company with six channels and three agencies spent three days a month stitching together reports that still could not be compared.
Related solutionThe situation
Google Ads, Meta, LinkedIn, GA4 and two search consoles had different metrics and different owners. A new spreadsheet was built every month, and leadership received answers that were already three weeks old.
What we built
- A data layer that collects approved sources through APIs
- Shared metric definitions for spend, conversions, ROI and ROAS
- An automatic report with deviations and explanations per channel
- Natural-language questions against the same data model
The result
The report is ready on the first working day and shows the same numbers as the ad platforms. Leadership asks follow-up questions directly instead of ordering another report, and agency numbers can finally be compared.
Let’s make it work