Built on receipts.
Not opinions.
Every engagement leaves your company a private record — tamper-proof, auditable, yours to take. These are the receipts. What we ship. What your team takes home.
Case I
NGO · 500 FTE · donor-funded
AI subtracts bloat. It doesn't add cost.
A donor-funded NGO ran the diagnostic over a stack of 22 active platforms. The fleet found half of it was load-bearing in name only — parallel CRMs, dormant dashboards, automations untouched in eighteen months. The recommendation was to cut. Output stayed flat over the 90 days that followed. Donor dependency dropped because burn dropped.
"The president didn't love the conclusion. His funding model depended on the appearance of complexity — bigger stack reads as bigger work. The lift was technical. The resistance was political."
- Stack cut
- 50%
- Output at 90d
- Unchanged
- Diagnostic clock
- 2 wks
Before
22 tools
After
11 tools
- ·CRM · Salesforce
- ✕CRM · Pipedrive
- ·Email · Klaviyo
- ✕Email · Mailchimp
- ·Tickets · Gorgias
- ✕Tickets · Zendesk
- ·Ops · NetSuite
- ✕Ops · Airtable
- ✕BI · Tableau
- ·Docs · Notion
- ✕Docs · Confluence
- ✕Forms · Typeform
- ▸CRM · Salesforce
- ▸Email · Klaviyo
- ▸Tickets · Gorgias
- ▸Ops · NetSuite
- ▸Docs · Notion
- ▸Storefront · Shopify
- ▸Ads · Meta
- ▸Identity · Okta
- ▸Files · Drive
- ▸Messaging · Slack
- ▸Finance · QuickBooks
Case II
MyGoat · sales agent · act 1 · 3–6 months ago
Three meetings out of 500,000 leads. The conclusion was the result.
MyGoat's pipeline leaned heavily on federal procurement and public-space airport contracts. Susan — the agent at the time, still immature — was asked to vet the entire prospect database and find any active booking link a sales rep could reverse-engineer into a meeting. She processed 500,000 leads in one week and found three. She set three meetings with Virginia airports without the prospects realising they were corresponding with an agent. On paper: a 0.0006% conversion rate.
The result wasn't the meetings. The result was the conclusion — the entire federal/public-airport lean was a dead end. The GTM pivoted the week after. A sales consultancy would have charged $25–75K for that conclusion and delivered a PowerPoint.
- Leads vetted
- 500K
- Token spend
- ~$150
- Conclusion
- 1 wk
What the AI found
Prospect database
500,000 leads
Active booking links
3
Meetings set (Virginia airports)
3
Strategic conclusion
Federal & public-airport lean · dead end · pivot the GTM
Case III
MyGoat · sales agent · act 2 · today
Susan stopped doing tasks. She started doing the job.
Susan today reads every sales-tagged email, call transcript and meeting note across MyGoat — including the ones the rep was just CC'd on. Pipeline scoring, deal retention, demand discovery, lead-gen, CRM audits — all running in the background. The new pattern is rep-initiated voice handoff:
"Just met Dallas airport. Fetch the content, relay the cost analysis sized to their org, send it by me first, and update the CRM."
The agent pulls the transcript, sizes the analysis from the diagnostic library, drafts the proposal in MyGoat's voice, holds at the approval checkpoint — "send it by me first" — then sends, writes the CRM, sets the SLA, and logs the full record of every action. The rep keeps the relationships. The agent keeps the receipts.
- Hours reclaimed
- ~20/wk
- Approval step
- 1 step
- Background tasks
- 0 human-sec
Voice handoff · five steps
- 01Pull meeting transcript + attendees
- 02Size cost analysis × your org
- 03Draft proposal in your voice
- 04Log a full record of every action
- GateHold for your approval
Case IV
MyGoat · token economics · ongoing
Same fleet. Same model class. Half the token spend.
At scale, the diagnostic fleet hit a cost ceiling. Per-action inference costs were climbing faster than throughput. The AI that does the real work treated this as an infrastructure problem, not a model problem — caching, intelligent routing across model tiers, prompt distillation across the fleet, and selective batch evaluation where latency allowed.
The same workload that previously cost $150 a week now runs at roughly $60 — same fleet, same model class, same fidelity on findings. The agent fleet now scales with traffic, not with model cost. That's the difference between a tool that costs you more as you grow, and infrastructure that gets cheaper.
"Inference is a metered utility. We treat it like compute, not magic."
- Per-workload
- $150 → $60
- Reduction
- ~60%
- Fidelity loss
- None
Running cost per workload
same fleet · same model class · same quality of findings
How we cut the bill stays proprietary. The AI that does the real work treats compute like any metered cost — caching, smart routing, and batching applied across the whole fleet, not one vendor trick.
Case V
Listed agriculture · publicly-traded · EU
BenchmarkedAI is the urgency. Infrastructure is the cure.
A publicly-listed agriculture company. Market in a hard cycle: thin margins, contracting order book, mother corp asking questions the board could not answer. A twelve-month death-date showed up in the projections. The board's first move was the obvious one — they fired the CEO.
The new CEO inherited the same death-date and reached for AI. An executive-level tool read every line of the order book and every transcript across two hundred board, sales, and executive meetings. It produced a clean diagnosis: the problem is systemic, the market is shrinking, the receipts are right. What it did not produce: a single piece of infrastructure. No workflow installed. No approval checkpoint live. No audit log written.
"The order book read itself. The company did not rebuild itself."
The new CEO has a brilliant read of why the company is dying. He is in the same chair his predecessor sat in nine months earlier. The death-date has not moved. AI is the urgency — fast, articulate, hungry for input. Infrastructure is the cure — slower, written down, signed, audited, queryable. One reads. The other operates. Companies that confuse them get a new CEO instead of a new operating model.
- Meetings ingested
- 200
- CEOs replaced
- 1
- Infrastructure built
- 0
The agriculture death-date · doom loop
read · diagnose · fire · repeat
Observed from outside the engagement. The pattern repeats across listed mid-caps in contracting markets — executives reach for AI tools to read the company, then the board reaches for HR to replace the executive. Infrastructure stays untouched.
The next names go here.
Our founding customers are in their first 90 days right now. Named stories — logo, the person who said it, the number it moved — land here once each company signs off on what we share.
- Story 1Logo and one-line result land here once the customer signs off.
- Story 2Logo and one-line result land here once the customer signs off.
- Story 3Logo and one-line result land here once the customer signs off.
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