Case Studies
14 production systems across 10 sectors, described by what was actually built and what changed for the people running the process.
Clients are described by sector rather than named, because most of this work sits inside a product our clients sell and naming it would give away their roadmap. Every system below is live work, not a proposal. If you want a reference conversation with one of these teams, ask us and we will arrange it.
Construction
2 engagementsReal estate
2 engagementsFinance and insurance
2 engagementsStaffing and HR
1 engagementEducation
1 engagementMarketing and media
2 engagementsSaaS and software
1 engagementProfessional services
1 engagementRetail
1 engagementCross-industry
1 engagementWhere the value actually showed up
Across every engagement above, the gain lands in the same five places. The tags on each card name which ones applied. None of them required replacing the team.
Consistency
The same input produces the same standard of output regardless of who is on shift, how busy the day is, or how long the person has been with the company. Rules that used to live in someone's head become part of the system.
Accuracy
Work is checked against the organization's own approved source material rather than against memory. Where the system is not confident, it escalates instead of guessing.
Throughput and coverage
Work that waited in a queue for a person begins immediately. Questions get answered outside working hours and at any volume, so capacity stops being tied to headcount and peak load stops being a crisis.
Institutional memory
Knowledge locked inside documents, inboxes, and long-serving staff becomes answerable. It stops leaving the company when people do.
Load on people
The one clients raise most and measure least. Staff stop absorbing the repetitive portion of the work by staying late. The queue that never emptied now empties. What is left is the part of the job that needs a person: judgement, exceptions, and the customer in front of them.
Where we keep humans in the loop
Decisions with legal, financial, safety, or reputational consequence stay with a named person. We build the approval gate in from the start rather than adding it after an incident.
What these engagements have in common
None of them started as an AI project. Each one started as an operational problem that had quietly become expensive: onboarding that could not scale, a reporting cycle eating an analyst team's week, a document estate nobody could search properly, a follow-up process that was the first thing dropped under load. The AI is the mechanism, not the point.
Three layers sit underneath all of them, and they go in this order. Get the organization's own knowledge into something a system can reason over and cite, with retrieval that can show its source. Give the system controlled access to the systems where the work actually happens, through integrations and purpose-built MCP servers. Only then hand it the authority to act as an autonomous agent, with the approval gates built in from the start rather than added after an incident. Skipping straight to the last step is the single most common reason AI pilots never reach production.
How an engagement runs
Every system above was delivered through the same five stages. The second one exists so that nobody commits budget to an assumption.
Discovery and process mapping
We sit with the people doing the work and map the process as it actually runs, including the workarounds. We identify where time is lost, where errors enter, and which steps genuinely need a person. The output is a written assessment, whether or not you proceed.
Where you can see this
Quote assembly became a process a manager can inspect, rather than an outcome only one long-serving person could explain.
Quoting and planning knowledge automationProof on one workflow
One real workflow, built properly against your own data, in a short cycle. Not a demonstration. The purpose is to establish whether the approach holds in your environment before the scope grows.
Where you can see this
A single estimation workflow, scoped tightly enough that two assessors could check the result against each other.
Estimation and assessment supportBuild and integrate
Production build connected to your live systems, with permissions, audit trails, approval gates, and failure handling designed in. Delivered in reviewable increments rather than one release at the end.
Where you can see this
Graded autonomy, hard stops, and a full audit trail were specified before the first agent was allowed to act.
Autonomous agents with defined authorityHandover and enablement
Technical documentation, runbooks, and training for your team, so the system becomes something you operate rather than something you depend on us to run.
Where you can see this
House style is held as editable configuration, so the client refines it without a redevelopment cycle.
Central AI content standardization serviceOperate and improve
Production monitoring, evaluation of model output quality, incident response, and an agreed cycle for extending the system as the business finds the next process worth automating.
Where you can see this
Every model call is observed and evaluated, so regressions surface as data rather than as user complaints.
AI-assisted recruitment operations platform
What we tell clients before they buy
AI removes the repetitive portion of a process. It does not remove the need to understand the process. Organizations that map the work honestly get a working system; organizations that automate a broken process get a faster broken process.
Your workflow is probably one of these
Book a free consultation and we will tell you which of these patterns fits, and what it would take to ship the first version.