This is how a project that used to take quarters ships in weeks — and every use case ships with proof it does what was promised.
We bring a delivery system alongside the engineering team: an AI-native delivery engine — a pipeline of specialized agents, prebuilt solution libraries, and automated quality gates, supervised at every step by senior engineers. It's the same engine we used to ship our own commercial product, and it runs every client engagement we deliver.
The result isn't just speed. It's consistency: every requirement traced to a use case, every use case to a build, every build to a test that proves it works.
Every engagement starts with what you already have — RFPs, requirements documents, discovery notes, even meeting transcripts. Our Requirements Agent normalizes all of it into structured requirements and use cases with acceptance criteria, flagging what's standard, what's custom, and what's ambiguous. If you have an existing Salesforce org, the OrgInsights Agent maps its health, technical debt, and license dependencies before we design anything.
Read every source document; produce structured requirements, personas, and testable acceptance criteria; flag what's standard, custom, or ambiguous.
Run discovery workshops, resolve the ambiguities agents flag, and make the scope calls only humans should make.
A requirements catalog you can challenge line by line — in days, before release a month of interviews.
Design starts from our solution libraries — prebuilt configurations refined across public sector and nonprofit implementations — giving the team a strong foundation from day one. Agents draft the solution architecture on native Salesforce objects wherever possible, keeping you on the platform's upgrade path and keeping configuration in your admins' hands after go-live. License dependencies are detected up front, so there are no surprise SKUs at deployment. Every design element traces back to the requirement that justified it.
Draft the solution architecture from the library, right-size the footprint to exactly what you need, and validate the design against your actual org before anything is built.
Architect the integrations, security posture, and the decisions that shape the platform for the next decade.
A design document with full requirement-to-design traceability — plus a working walkthrough by week 3.
Build Agents configure and generate the solution from the design: forms, flows, portals, components, integrations. Domain libraries and the LPI Accelerator mean 75% of core solution is pre-built and ready to go on day one — so our engineers spend their time on what makes it yours. Every change ships through an automated build-test-deploy loop, with quality gates and engineer approval before deployment. Work is delivered in demoable two-week slices, so you watch the system grow instead of waiting for a big reveal.
Generate configuration and code, write the tests alongside, run the deploy loop, and fix what the gates catch.
Review every generated component, own the complex decisions, and approve every deployment.
A working demo at the end of every sprint — and a change log your team can audit.
Every use case ships with plain, testable acceptance criteria, and agents turn those criteria into executable tests against your actual org. Our quality gates check the big things and the small ones: security and access by persona, end-to-end functionality, and the finishing details your users notice on day one. A use case is complete when its criteria pass. At handover, you receive the evidence alongside the system.
Convert acceptance criteria to executable tests, run them against the org, and report pass/fail per criterion.
Design UAT with your team, triage what testing surfaces, and sign off only on demonstrated results.
A pass/fail report mapped to every requirement — proof your system does what was promised.
AI autonomy works best with clear engineering accountability. Every agent in our pipeline operates inside human approval gates: engineers review generated designs before they're presented, approve every deployment before it runs, and stand behind every outcome. Your data stays in your org. Our agents inherit only the permissions you grant. Everything they produce is logged, traceable, and auditable.
We don't ask clients to trust a pipeline we wouldn't bet on ourselves. Our own AppExchange product — EasyMask — was built, tested, and security-review-prepped by the same engine. The pipeline got sharper because a product's quality bar demanded it. Every project hardens the engine; every release makes our delivery faster. Read the case study →
Three engagements — one internal, two client — that show what the pipeline actually produces.
EasyMask, our native Salesforce data-masking package, was built end-to-end with the AI-native delivery engine. We were customer zero: proof-of-concept on a commercial product before we offered the engine to any client.
Read the case study →A state labor & workforce agency replaced a decades-old trade-certification system — 24 license types, 400,000 records, full SSO integration — in 17 weeks, with a working org by week 3.
Read the case study →Five city departments, 14 permit types, a resident portal, inspector mobile, and map-based code enforcement — built on Salesforce Public Sector Solutions in three weeks.
Read the case study →Delivery, data access, go-live, and more — now collected with our other customer questions.