95% of company AI projects never make it out of the demo. We are the senior engineers who build the 5% that reach real customers — with accuracy testing, safety checks, and reliability built in from day one.
The demo is easy. Correct, safe, affordable, and reliable for real users is the hard part — and exactly where the 95% stall. We handle all six.
Automated tests that check the AI’s answers, so quality is measured, not hoped for.
Catches wrong or unsafe answers, and blocks attempts to trick the AI, before anything reaches a user.
The AI answers from your own documents and data, tuned for relevance, so it is grounded in facts, not guesses.
We measure what each request costs and keep it fast and affordable at real scale.
We watch quality in real time and flag problems in minutes, not when a customer complains.
Automatic backups and graceful handling of failures, so an outage on their end never becomes yours.
Tap what you already have in place and watch your readiness score move. Then get the full picture in two minutes.
Eight quick questions on the six things that decide whether an AI is ready for real users. You get a grade, your odds of a successful launch, and the single most valuable thing to fix first — right here on this page.
Most teams begin with the free check, move to a fixed-fee audit, then a build — and keep us on afterward to keep it running reliably.
See your grade and your number-one blocker in two minutes. Free, self-serve.
We review your real system and hand you a scored readiness report, ranked findings, and a fixed plan to fix them. A workable plan to launch, or you do not pay.
We do the work end to end or alongside your team: accuracy testing, safety checks, connecting it to your data, cost control, integration. Priced on value, not hours.
Ongoing testing, monitoring, and tuning so it stays accurate after launch.
You work directly with the senior engineer who does the work — no account managers, no junior hand-off, no bench.
Tovasol replaced a 13-week, multi-person program with a single day of AI-driven delivery — one engineer, the same outcome. The machine does the work of a team, then runs your AI on it.
Branch-per-environment CI/CD (dev → qa → prod) on gitolite + rootless Docker, sops/age tiered secrets, and Terraform-managed Cloudflare infra that bootstraps its own state.
A capture-intent lead pipeline on D1 with versioned migrations, transactional email, and edge rate-limiting — a backend he owns end to end.
Evals-first correctness, browser E2E on every deploy, and WCAG-AA accessibility — so quality is measured, not hoped for.
JSON-LD machine-legibility and first-party AI-crawler visibility, so answer engines like ChatGPT and Claude can cite your product.
We agree in writing on what a successful launch means before we start. If the audit does not give you a defensible plan to get there, you do not pay. That is how confident we are in the work.
A clear, low-commitment path. You can stop after any step, and you know the scope and price before the paid work starts.
20 minutes on your actual system. We find where it breaks and whether an audit is even worth it.
We agree the scope, a fixed price, and what “ready to launch” means — in writing — then review your real system.
A scored report, ranked findings, and a fixed plan to fix them. A workable plan to launch, or you do not pay.
We do the fixes end to end or alongside your team, then keep it accurate after launch with ongoing testing and monitoring.
Pick a time that works. No prep needed — bring the AI project that is stuck.
A working session on your actual system — not a sales deck. We find where it breaks and name the exact thing standing between you and launch.