Case studies
Results, not just deliverables
How we turn AI agents, automation, and custom AI into outcomes a CFO can verify — and how we document them.
The AI services industry has a case-study problem: anonymous logos, unverifiable percentages, and results that evaporate under a single follow-up question. We hold ourselves to a stricter standard. Every figure on these pages is taken from the delivered system itself — tests passing, volume processed, before-and-after runtimes we measured — not from a rounded-up guess about business impact.
That standard cuts both ways. Where a client hasn't shared a verified business outcome, you won't see one invented to fill the gap: we describe what was built and what it demonstrably does, and leave it there. Client engagements are described by project rather than by name, because most of this work sits inside systems our clients would rather not advertise. The section below explains exactly how we measure, so you know what to expect from us — and what to demand from anyone else you evaluate.
Social DM Autoresponder
Answering a consumer brand's Instagram and WhatsApp messages automatically
Inbound Instagram and WhatsApp messages for a direct-to-consumer food brand, answered automatically from a single self-hosted pipeline.
Project Firmament
An AI operating system a business gets plugged into, not built around
A business-agnostic AI operating system: one governed work-order lifecycle, seven agents on dedicated hardware, and an immutable decision ledger — designed so a new business is onboarded rather than rebuilt for.
Autonomous Voice Follow-Up & Revenue Portal
AI agents that place the calls, book the appointments, and report on themselves
Outbound AI voice calls that book straight into the CRM, messaging agents on a bounded tool loop, and a portal that projects whether the quarter lands.
Dialer Intelligence Pipeline
From 118,000 unattended calls to 401 qualified leads in the CRM
A backlog of 118,084 unreviewed sales calls, triaged and written into the CRM as scored leads — with 98% of volume filtered before any model ran.
Autonomous Six-Agent Operations Team
Six AI agents that answer from the founder's own knowledge — and run overnight
A 24/7 agent team grounded in a retrieval layer over the founder's own material — plus the reliability engineering that diagnosed three silent production outages.
Strategy Stack
Turning a blank AI chat window into a configured workspace, thirteen plugins deep
A commercial 13-plugin product line that interviews a knowledge worker and generates their entire AI workspace, with twelve profession-specific packs.
Automated Government Bid Pipeline
From 40 solicitations a week by hand to an eight-step automated bid pipeline
An eight-step pipeline that finds government solicitations, extracts line items from mixed-format documents, prices them against live distributor feeds, and assembles the bid package.
Proposal Mirror & AI Document Builder
Getting a company's proposal library out of a tool that had no export
An automated export path where the vendor offered none, plus an AI document builder returning schema-validated proposals with automatic repair.
Content Pipeline Audit & Rebuild
An AI content pipeline that publishes — audited, rebuilt, and handed back working
A 57-node content pipeline audited and rebuilt around human approval gates, taking two workflows from a 0% success rate to publishing end to end.
CloutIQ
Scoring a video before it's filmed
A platform that scores a short-form script before production — viral scoring, predicted retention curves, rewrites and distribution packs.
Our standard
How we measure results
Four rules that govern every number we ever publish — or claim in a sales call.
Baseline before build
Before any system ships, we record how the process performs today — hours spent, error rates, response times, revenue leakage — so 'better' has a number attached.
Instrumented from day one
Every system we deploy ships with its own measurement: tasks completed, accuracy against review, escalation rates, cost per task. Results come from dashboards, not recollection.
Honest attribution
We separate what the AI changed from what would have happened anyway — seasonality, staffing changes, process fixes — rather than claiming every uptick as ours.
Measured, or not claimed
If we can't point at where a number came from, it doesn't go on the page. We'd rather publish what a system provably does than an impressive percentage nobody can trace.
The metrics that matter, by system type
Different AI systems earn their keep differently, so we anchor each engagement to the metric that fits. For AI agents and automation, that's hours of manual work retired, error rates versus the human baseline, and cost per completed task against the labor it replaces. For voice agents and chatbots, it's answered calls, booked appointments, resolution rate, and CSAT — not vanity "deflection" numbers that just mean customers gave up. For RAG and knowledge systems, it's answer accuracy on a golden question set and time saved per lookup. For machine learning, it's prediction accuracy against the method you use today, translated into dollars of decision improvement.
We agree on the metric in discovery, before a line of code is written. It becomes the definition of success in the statement of work — which is why our case studies, when they publish here, read like measurement reports instead of marketing copy.
Want to be a result we can publish?
Tell us what you're trying to achieve. We'll define the baseline, the metric, and the fastest path to moving it.