Hire the Engineer,
Not the Agency.

A large firm sells you the partner and staffs you with juniors, bills a team of 8 to 12 people through months of discovery, and, according to industry surveys, sees roughly half of AI projects fail to reach production. Here, the senior engineer who scopes your system also architects, builds, and ships it. The record so far is 7 systems proposed and 7 in production. You get working software in weeks at a fraction of the cost.

You probably should, eventually. Recruiting a senior AI engineer takes months, and a new hire needs more months to ship a first production system. The faster path is to get a system live in weeks and then let your team take it over. Full source code, documentation, runbooks, and team training are part of every handover, so your future hires inherit a working system instead of a blank page. Many teams use the delivered system as the starting spec for their next hire, and some pair the build with a fractional AI lead engagement so in-house capability grows while the system is being delivered.

The documented range across shipped systems is 4 to 8 weeks. A natural language query engine for a cybersecurity platform took 4 weeks, a HIPAA-compliant appeals system took 5, full-coverage call center QA took 6, and a company-wide AI platform serving more than 200 locations took 8. The speed comes from AI-assisted delivery. The codebase is absorbed in 24 to 48 hours instead of months of knowledge transfer, the full production architecture is ready for your approval by day 5, and a working proof of concept usually lands within the first week.

Every engagement is fixed price and scoped before work begins. There are no hourly meters and no surprise overruns. There are three ways to start: a fixed-price proof-of-concept sprint that delivers a working prototype on your data in about a week, a fixed-scope production build that typically runs 4 to 8 weeks, or ongoing advisory as a fractional AI lead. You will have an exact number after the free assessment, before any commitment. And if the numbers do not support building, you will be told exactly that.

The engagement structure is designed around that risk. You see a working proof of concept running on your own data before committing to a production build. Builds are fixed scope with working software delivered into your environment every week, so there is no surprise at the end. You always know where things stand, and you own every artifact at every stage, so nothing is lost if you stop. The track record so far is 7 systems built, 7 shipped, and none requiring rework.

You do, completely. Source code, prompts, evaluation suites, documentation, runbooks, and infrastructure configuration are delivered into your repositories as they are written. There are no black boxes, no license-back clauses, and no dependency on SASID to maintain or extend the system. If we parted ways the day after launch, your team could run and extend everything on its own. Team training is part of the handover.

Systems have shipped into regulated environments, including HIPAA-compliant healthcare delivery under a signed BAA, where the medical appeals system integrates EPIC and Cerner over HL7 FHIR with full PHI safeguards, as well as architectures built to SOC 2 and ISO 27001 requirements inside client environments. PII redaction, audit trails, guardrails, and observability are standard in every build. NDAs are signed before you share anything, and work can run entirely inside your cloud on your own accounts. To be precise, SASID builds systems that pass client security reviews; it does not claim certifications it does not hold. Your security team is welcome on the first call.

No. Your data and code are used only to build and evaluate your system. Nothing is used to train models, and the commercial API tiers these systems are built on contractually exclude your data from provider training. If you commission fine-tuning on your own data, the resulting model weights belong to you. Where requirements demand it, everything runs inside your cloud with your keys, and your data never leaves your environment.

Every build includes 90 days of post-launch monitoring and optimization. Accuracy, cost, and performance are tracked and tuned proactively. After that you choose how to continue: run the system entirely in-house with your trained team and full documentation, or keep a fractional AI lead retainer for roadmap planning, architecture reviews, and new capabilities. There is no lock-in either way.