What We Do

We replace the traditional requirements workshop with AI-guided spec interviews conducted through Daefodil. Our platform interrogates your use case across entities, roles, workflows, and edge cases, producing a structured specification that is both human-readable and machine-consumable. The spec is versioned, queryable, and evolves with your business.

How AI is leveraged: Claude-powered interview sessions extract and structure requirements with a completeness no human analyst can match in the same window. Pattern-matching against our 13+ reusable capability patterns (offline-first, validity-dated records, biometric attendance, and more) surfaces implicit requirements you would otherwise discover in production.

Weeks of requirements workshops collapse into a validated, compilable spec in 5–10 days.

The specification feeds directly into our deterministic code-generation orchestrator. Database schemas, API contracts, service layers, and UI scaffolds are produced from the spec in a single pass, with compilation and middleware-awareness gates built in. What emerges is not a slide-deck prototype — it is running software.

How AI is leveraged: Our code-generation pipeline uses Claude Code CLI under deterministic orchestration — eliminating the non-determinism that makes most LLM-based codegen unreliable in production. Scaffold placeholder replacement, compilation validation, and per-section compilation ensure the output is real, not rehearsed.

A 4-week prototype cycle becomes a first working build in days.

Every generated system is wired through Axiom Gateway, our AI governance and authority platform. This gives you row-level security, zero-persistence data handling, customer-managed encryption, declarative authority matrices, and full audit trails from day one. Security and compliance are not a later phase; they are a compile-time property of the code we ship.

How AI is leveraged: AI-assisted code review, automated SQL injection and authorization checks, and continuous policy validation against the Authority Matrix mean governance violations are caught before deployment — not discovered during an audit. Platform-level response caching (SHA-256 keyed, per-feature TTL) also keeps AI inference costs predictable at scale.

Enterprise-grade governance inherited at startup-grade cost and timeline.

We launch into AWS, Azure, or your preferred environment via Yinatra, our infrastructure routing layer that supports a bring-your-own-cloud model. Deployment is containerized, observable, and reversible. Our operations layer covers everything from ECS Fargate orchestration to the post-deploy knowledge sync that keeps your specs and running code in lockstep.

How AI is leveraged: AI-assisted runbooks, anomaly detection on production traffic, and automated remediation for the most common incident classes reduce your operational burden. When an issue does require human judgment, our telemetry surfaces it with enough context that resolution is measured in minutes, not hours.

You go live with production confidence — and stay there with AI-amplified operations.

Post-launch is not a handoff; it is the beginning of the relationship. Your spec keeps evolving, your codebase regenerates against it, and your product matures without the typical technical-debt tax. Our subscription tiers — Build, Launch, Grow, and Scale — align our commercial model with your maturity curve, not a fixed seat count.

How AI is leveraged: Continuous spec evolution through Daefodil means feature additions are generated the same way the initial system was — no architectural drift, no decay. Usage telemetry feeds back into the spec, so the AI surfaces which capabilities to invest in next based on real user behavior.

You stop buying projects and start compounding a product.

We inventory the data assets that actually run the business — sources, owners, latency, lineage gaps, governance debt. The output is a current-state map honest enough to plan against, paired with a target-state architecture that fits the org's operational reality, not a reference-architecture wish list. Decisions get surfaced, not deferred.

Lakehouse pattern, governance model, BI reachability, cost ceiling. We default to Databricks plus Unity Catalog as the platform spine — Delta for storage, MLflow for lifecycle, Unity Catalog for governance — and support alternative platforms when the existing stack dictates. Architecture decisions are documented with the trade-offs, not just the end state.

Big-bang migrations fail. We sequence migrations so the legacy and modern platforms run in parallel, governance is baked in from phase one (not deferred to a later compliance project), and each phase delivers an independently usable slice. If a phase needs to pause, the business keeps running.

Controlled cutover with parallel running and validated reporting. Stakeholders see new-platform outputs reconcile against legacy-platform outputs before legacy is retired. No silent regressions; no surprise month-end. The legacy platform is decommissioned only after the modern platform earns the workload.

Post-cutover is the start of the relationship, not the end. SLA-backed pipelines, lineage and quality observability, cost optimization, and a DataOps practice that keeps the platform getting better as the business grows. You don't get handed a dashboard and a goodbye.