AI services that reach production

The highest-priority focus. AI test-case generation from source code and requirements, self-healing test automation, end-to-end and UI testing, automated regression, and coverage uplift wired into the pipeline. Quality scales with the codebase rather than lagging it.

Developer productivity done properly. AI pair programming, refactoring, and documentation, with enterprise rollout, governance, and a tool evaluation framework across Copilot, Cursor, and Codium. Your engineers move faster with guardrails they trust.

Agentic code review with enforceable standards that every developer and AI agent follows, security scanning, breaking-change detection, and audit trails across the pipeline. Speed and governance in the same workflow.

Design, evaluate, and defend enterprise-grade AI architectures. RAG, agents, MCP, multi-agent systems, vector databases, and LLM orchestration, with quality attributes and trade-offs made explicit and decisions you can justify to any stakeholder.

The data foundation AI depends on. Pipelines, quality rules, structured attributes, lineage, and governance, delivered as an AI-ready data MVP in one or two priority domains, then scaled. Trustworthy inputs make trustworthy AI.

Agentic AI and LLM-powered product features taken from prototype to production-grade, with evaluation, guardrails, and observability built in. The features customers notice, shipped on a foundation that holds.

Grounded retrieval pipelines with vector search, reranking, and citations, so every answer traces back to your structured knowledge and stays accurate and explainable.

Evaluation harnesses and LLM-as-judge across RAG, agent, safety, and custom evals. Offline evals become production guardrails, and quality improves measurably week over week.

Runtime guardrails that catch hallucination, prompt injection, jailbreaks, PII leakage, and off-topic drift before any output reaches a customer. Safety enforced at runtime, aligned with your governance policy.

AI-enhanced search and guided discovery, recommendations and guided selling, and grounded shopping copilots. Every customer-visible output traces to governed product data, with a human in the loop on brand voice.

Process inventory, knowledge consolidation, and a skill registry with named owners and a learning loop, applying 80/20 automation discipline so capability compounds across the organization rather than resetting with each project.

The six back-office plays for retail and commerce: GEO and AI discoverability, dynamic pricing, product enrichment, supplier listing automation, marketplace growth, and campaign personalization. Built from deep retail experience.

Governed product data as the foundation for trusted AI. A data-readiness audit, governance design, and AI-assisted enrichment on a Draft, Validate, Publish pipeline that keeps a human in control of accuracy.

Campaign personalization off live data, content generation, segmentation, lead scoring, proposal automation, and pipeline support, tied to your CRM and brand voice.

Support automation, deflection, intelligent routing, and agent assist that shorten resolution times while keeping a human in the loop on every customer-facing answer.

Document processing, reconciliation, anomaly detection, and supplier intake automation that turn days of manual work into minutes of exception routing.

Instrumentation across the Activity, Anchoring, and Impact maturity model, so you measure time saved and net-new capabilities and prove the value at 6 and 12 months.