Forward Deploy · Outcome-Based
We embed. We don't advise.
One senior engineer in your operation. Running AI tools that do the output of a team of six. No hiring. No transformation programme. No slide deck at the end.
Start with the audit
How it works
The forward deploy motion
Keystone does not send a team. It sends one senior engineer running an AI framework — scanning your codebase exhaustively, finding what six people used to find, writing the fixes, raising the PRs.
The sophistication of the full suite is why the buyer stays. The concreteness of one engineer outperforming your QA team is why they start.
1
Exhaustive codebase scan
AI reviews every line at speed no human team can match. Findings logged in real time.
2
Bug identification and fix
Critical issues found, fixes written, PRs raised. Your team reviews and merges.
3
Architecture and security review
Running CTO, CISO, and architect-level analysis in parallel — not five hires.
4
Agent builds and automation
Recurring tasks identified, agents specced and shipped. Maintained as models drift.
5
Audit-ready documentation
Every finding documented to compliance standards. SOC 2, HIPAA-mappable output.
THE PROBLEM
Most mid-market companies have invested in AI tools, run pilots, and hired engineers who understand the technology.
None of it has moved the numbers.
The reason is structural. The tools are real. The engineers are capable. What is missing is the piece that makes it all hold — the human judgment layer that decides where AI plugs in, what it touches, and what happens when it's wrong.
The arch is built from both sides. The keystone has not been placed. The scaffolding is still doing the work.
Team replacement
At the entry level we compete with your QA team. One Keystone engineer outperforms a traditional team of six — at a fraction of the cost, with higher coverage and faster turnaround.
Traditional model
A QA team built the old way
/yr
7 US engineers — fully loaded
The Keystone model
One engineer. AI-amplified.
/mo
Outcome-priced. Deployed in days.
What we offer
No hourly rates. No day rates. We price against what we find and what we fix. The audit fee converts to credit if you proceed.
01
Entry point
fixed fee
02
Lead wedge
per issue resolved
03
Output-based
per agent shipped
04
Long-tail revenue
MONTHLY
One engineer. Five virtual experts.
What the framework runs in parallel.
CTO
$400–600k/yr to hire
CISO
$350–500k/yr to hire
Architect
$250–380k/yr to hire
Quality Lead
$180–260k/yr to hire
White-Box Tester
$160–240k/yr to hire
Annual cost to deliver the same outcome
The cost of senior engineering oversight has not changed. What has changed is how much of it needs to be human.
Time to value
Traditional hiring approach
1
Week 1-6
Job specs and sourcing
Define roles, brief recruiters, build pipeline for 5–6 positions simultaneously
2
Weeks 6–14
Interviews and offers
3
Weeks 14–24
Notice periods and onboarding
4–12 week notice periods, access provisioning, team integration, ramp-up
4
Month 6+
First meaningful output
Team aligned, tools set up, processes established. First real findings delivered.
KEYSTONE
1
Day 1–3
Scoping call + codebase access
Scope confirmed, engineer assigned and briefed, framework deployed. No hiring.
2
Week 1–2
Full scan begins immediately
Five virtual personas running in parallel. Findings logged and prioritised in real time.
3
Week 2–4
Diagnostic report delivered
Full findings with severity tiers, cost estimates per issue, and fix roadmap.
4
Week 4–6
First agents in production
Critical fixes shipped. First agents deployed. Audit-ready documentation in hand.
5
Week 6
First fixes merged, audit-ready documentation in hand
Return on investment — worked example
What a $40M company typically finds.
A representative engagement across mid-market software businesses at the $30M–$60M revenue range.
AI Readiness Audit
$20,000
$40,000
$75,000
$150,000
What it recovers
Redundant labour recovered
$420k
Breach risk reduction value
$900k
Engineering velocity gain
$280k
Total recovered value
5.6× return on year-one investment
What a typical scan finds
A Keystone scan of a mid-market SaaS codebase (250k–1M lines) typically surfaces findings across four severity tiers within two weeks.
Medium
Low
All findings come with severity assessment, cost to fix, and prioritised resolution order. Tier pricing approved upfront — no surprise invoices.
Cache accessed simultaneously by multiple threads — causes intermittent data corruption under load. Found in a single scan today on a real production system.
Improperly validated headers allow privilege escalation. Invisible in normal testing, exploited by automated scanners in hours.
Event listeners never deregistered — causes gradual memory leak that crashes production under sustained load.
Production credentials in repository history. Accessible to anyone with repo access, including former employees.
The objection
For a weekend code review, you should. For a production system thousands of people depend on, the distance between "an LLM can look at this" and "an LLM can systematically protect this" is where companies lose millions.
01
02
03
04
Market validation
Building defensibility in the software layer on top of the models is going to be incredibly difficult. It is the ability to layer services on top of software — going the last mile with the customer, the forward deployed motion — that is creating stronger defensibility.
Brendan Foody, CEO
The audit fee converts to credit if you proceed. If the findings don't justify the next step, you walk away with a roadmap worth more than you paid for it.
Book a 30-minute call