Claude Code, Codex, Cursor and Devin
Kalmantic / Outcome-led software delivery
Ship more customer-ready software.
We commit to a production outcome, operate the redesigned workflow, and transfer the resulting system to your team.
The current state
You have already bought AI speed.
The investment already exists across coding agents, engineering talent, QA, infrastructure and executive attention. The question now: did that investment change customer-ready release?
Repositories, CI/CD, cloud and test infrastructure
Training, prompt libraries and process experiments
CEOs and CTOs redesigning delivery by hand
The return gap
Your developers got faster. Did production?
More code and more PRs create value only when they become customer-ready releases.
Code outputProduction flow
PlanBuildReviewTestRelease
The bottleneck moved.The workflow did not.
Observed across software teams
The same pattern appears at different scales.
80–90% on Claude Code. 3–5× POC speed. Review and QA remain manual.
About $10K monthly Claude spend. Merged PRs doubled. Safe autonomy remains unresolved.
$36K monthly Claude spend. 3× reported output. Regression and test data constrain delivery.
Observed customer-discovery patterns. These are not Kalmantic-produced outcomes.
The engagement starts here
A committed production outcome.
A production outcome measures customer-ready work reaching production. It does not measure tokens, prompts, code generated or agent activity.
Production-ready stories per month
Approved requirement to production
Safe releases per week
Stories reaching production with defined human intervention
One primary metric becomes the commitment.
CEOCustomer commitments shipped
Can the company deliver its roadmap and new-market promises faster?
CFOProduction output from the existing budget
Does the current engineering investment produce more finished software?
CTORequirement-to-production flow
One shared scoreboardWhere does work wait, fail or return for rework?
Before the commitment
The baseline makes the outcome credible.
We trace one workflow from approved requirement to production and measure where work waits, returns or requires scarce human judgment.
Baseline = throughput + lead time + rework + human intervention- 01ApprovedInput accepted
- 02PlannedGaps resolved
- 03BuiltChange complete
- 04VerifiedCorrectness proved
- 05ProductionCustomer-ready
Example commitmentIncrease production-ready stories from 20 to 30 per monthwithin 90 days
Or: approved requirement to production from 20 days to 12. The target is set with you after baseline. The metric is production. Never tokens, PRs or activity.
The operating commitment
Diagnose. Redesign. Operate. Transfer.
Diagnose
Weeks 1–2Trace the workflow, set the baseline, and find the constraint.
Redesign
Weeks 3–6Change the workflow. Build only what moves the metric.
Operate
Weeks 7–12Run it with your team until the number holds. Showcase every week.
Transfer
Week 12Source, runbooks, metrics and a named internal owner. Yours to keep.
The guarantee
If we miss the number, you do not pay for it.
Every pilot on the market charges for activity and hopes for a result. We charge for the result. The risk of being wrong sits with us.
Not earned unless the agreed metric hits the agreed target.
Past 90 days at no charge, until the number holds or you call it.
Source, agents, evals and runbooks. Hit or miss, it stays with you.
Model and compute cost through the window is ours, not a pass-through.
The price, printed
$190K for 90 days.
A third of it only if the number hits.Baseline, workflow redesign, first build. Invoiced when the operating foundation is delivered, week 6.
Three months. Founders embedded, weekly showcase. Invoiced monthly while Kalmantic operates the workflow.
The committed production result. Earned only when the agreed metric reaches the agreed target.
No predetermined tool
The intervention follows the constraint.
We do not sell a generic automation recipe. We change the part of the production system that limits customer-ready output.
Requirements and gap resolution consume the cycle
Corrections disappear after each agent session
Generated code overwhelms senior reviewers
Developers seed fixtures and environments manually
QA queues absorb the gains from faster coding
Deployment and service coordination remain human-bound
What remains with the customer
A customer-owned agentic software factory system.
The path from approved work to production
Customer-specific builders, reviewers and operators
Evidence that defines and proves correct
Corrections that persist into the next run
Human approval where judgment still matters
Runs on OpenFactory, the workspace your platform team keeps. Models will change. The system stays under your control.
Proof
Already built, already running, already paid for by someone else.
Factory stations, maker-checker agents, a swappable harness and adaptive documentation. 11% measured experience uplift. Paying design partner since August 2026.
Built and shipped by the founders. 53 builders, 3 design partners. Every engagement leaves it installed.
Installed at a 20-person team running 70–80 PRs a week on 9–10B tokens a month. Their first usage number.
Case study / Nuvepro
Faster production of customer-specific sandboxes.
An engineer studied the technology, built the sandbox, tested it and prepared the supporting material.
1–2 days per sandboxNew technologies arrive faster than a manual, customer-specific production process can absorb.
70–80% AI-generation ambitionFactory stations, maker-checker agents, swappable components, terminal re-engineering and adaptive documentation.
11% measured experience upliftThe first architecture experiment outperformed the previous RAG approach. A second experiment is underway.
Low regret by design
Your team needs us less.
One workflow, your code, 90 days. If it fails there is little to unwind. If it works you hold the system and the evidence to fund the rollout.
Customer-owned code and deployment configuration
Workflow definitions, runbooks and production metrics
Evaluations, controls and evidence requirements
Training and a named internal platform owner
Why Kalmantic
Product research, embedded execution, production accountability.
Kalmantic builds inference infrastructure, coding agents and self-improving harnesses. Founder-run, three engagements at a time. The people here are the people in your repo.
Business outcome, executive alignment and commercial accountability.
Technical architecture, system construction and embedded operation.
Customer workflow, adoption and operating change.
The first decision
Which production outcome matters now?
Choose one workflow. In the first working session we baseline it, find the constraint, set the 90-day number and print the price.