Estimation is a skill. AI can learn it.
Every agency does the same thing when a new brief arrives: someone senior reads it, pulls up old project folders, and spends hours producing a number that is half art, half memory. CostLabsHQ replaces that muscle memory with a structured, calibrated, reproducible pipeline that turns a one-paragraph brief into a full proposal in 60–90 seconds.
The goal is calibration, not automation. Humans still close the deal.
Lambda pipeline. One estimate.
CostLabsHQ runs on AWS as a fleet of Lambda functions behind API Gateway. Each Lambda owns one phase of the estimation pipeline, with a containerized worker handling the heavier Claude inference steps.
- Intake — brief parsing → NormalizedIntake JSON
- Research — market signals + competitor landscape (web + GitHub)
- Code audit — existing codebase quality → estimate discount / risk buffer
- Cost model — deterministic hour bands [low, mid, high] per milestone
- Output — multi-tier dollar-window model + branded proposal HTML/PDF
- Analytics — 7 events per session, daily digest, zero third-party tracking
- Cache — SHA-256 fingerprint DynamoDB cache — same brief = instant re-use
A range, not a number.
Rather than a single number (which is always wrong), CostLabsHQ produces tiers representing the range from bare-bones MVP to full-featured production launch. Each tier has a dollar window, milestone count, and risk profile. Calibration clamps keep estimates within ~15% of human-authored ballparks.
- Skeleton — bare MVP
- Slim — core flows only
- Mid — production-ready
- High — polished + integrations
- Full — enterprise-grade
Live on AWS.
CostLabsHQ is deployed and processing real estimates. Claude Sonnet cross-region inference profile. Worker container on ECR. SES email delivery with branded proposal attachments. Daily analytics digest.
View live project