AI Systems That Run
in Production.

• AI fluency workshopsBring leadership and teams to a working understanding of what AI does and doesn't do for your business.
• AI-native enterprise strategy and roadmapA sequenced, costed plan tied to business outcomes, not a technology wish list.
• AI security and risk strategyDefine your risk posture, guardrails, and acceptable-use boundaries before deployment.
• AI-native organization designRestructure teams, roles, and decision rights for a business where agents do a meaningful share of the work.

A funded roadmap your board understands and your engineering team believes in.

• Data foundation and engineering
• AI-ready data foundation and lakehouse creationOne governed foundation serving analytics, applications, and AI off the same source of truth.
• Real-time change data capture at scaleData that reflects the business as it happens, not as it stood at last night's batch.
• OLTP and OLAP convergenceOperational and analytical workloads on one architecture, without the copy-and-sync sprawl.
• Vector and full-text hybrid search at scaleRetrieval that handles meaning and exact match together, across your whole corpus.
• Self-service and conversational analytics
• Semantic layer and metrics-store engineeringOne definition of every metric, so AI and humans compute the same number.
• Conversational, agentic BINatural-language questions answered against governed data, with the reasoning shown.
• Voice-based reporting and queryingAsk across your data stores and get answers, hands-free.
• Predictive and decision intelligence
• Agentic analyticsAgents that analyze, decide, and trigger the downstream workflow, rather than surfacing a chart and waiting.
• Scenario simulation and what-if analysisModel the consequences of a decision before you make it.
• Threshold, drift, and exception alertingOperational and risk events surfaced as they emerge, not in next month's review.

Data that operates the business, not just reports on it.

• Open-weight model deployment, fine-tuning, routing, and distillationRight-sized models, tuned on your data, running where policy requires.
• Small language model developmentPurpose-built models for specific tasks. Faster, cheaper, and often more accurate than a general-purpose frontier model.
• Advanced training methodsRLVR, GRPO, constrained reinforcement learning, reward design, and synthetic data generation for domains where off-the-shelf training data doesn't exist.
• Model lifecycle managementRetraining, drift detection, and version governance, so the model that passed review is the model in production.

Full control of your AI stack, with no dependency on one vendor's roadmap or pricing.

• Agentic RAG with plan-adaptive retrievalRetrieval that reasons about what it needs before it searches, instead of pattern-matching one query.
• MCP-style semantic access layersA standard interface for AI to reach structured and unstructured sources, without a bespoke integration per system.
• Ontologies and domain-specific knowledge graphsEncode how your business relates its entities, so AI reasons in your terms.

AI that answers from what your organization knows, with traceable sources.

• Enterprise agent network and operationsA managed fleet with shared standards for identity, tooling, permissions, and monitoring.
• Multi-agent orchestration and durable, long-running agentsAgents that carry work across hours, days, and systems without losing state.
• Durable agentic harnessesThe runtime scaffolding, retries, fallbacks, human escalation, and audit trail that make agents dependable enough for real work.
• Memory architectures and context window managementWhat an agent remembers, forgets, and retrieves, which separates a system that improves from one that degrades.

Agents running production workflows, not scripted demos.

• Eval-driven development and continuous evaluation in CI/CDEvery change to a prompt, model, or tool is tested against your benchmarks before it ships.
• Eval monitoring, AI attribution, and drift managementKnow when quality moves, and know which component moved it.
• Domain-specific benchmarks for real business tasksMeasured against your work, not public leaderboards with no bearing on your outcomes.

Quantified AI performance you can put in front of your board.

• AI security strategy and governance roadmapA defensible position on how AI is permitted to operate in your enterprise.
• Secure AI reference architecture implementationSecurity designed into the architecture, not layered on after the build.
• Agentic threat modeling and red teamingAdversarial testing against the specific ways agentic systems fail.
• PII anonymization before LLM accessSensitive data protected before it reaches a model.
• Governance and continuous monitoringOngoing oversight, audit trails, and evidence for regulators and internal audit.

AI deployments that clear security, legal, and compliance review the first time.

• AI-native SDLC transformationRebuild your delivery process around AI-assisted development, review, and release.
• Agent-based testingCoverage generated and maintained by agents, at a depth manual QA can't sustain.
• AI-accelerated legacy modernizationUnderstand, document, and rewrite legacy systems at a fraction of the traditional cost and timeline.
• SaaS-to-AI-native product re-architectureRe-architect your product for a market where users expect it to act, not display.
• Managed AI-native deliveryOur teams deliver against your outcomes using AI-native methods end to end.
• AI-native custom builds replacing seat-based SaaSPurpose-built systems that replace per-seat licensing you've outgrown.

Faster delivery, a shrinking legacy estate, and a software cost base that stops scaling with headcount.

• AI-native CX strategy and customer-journey redesignRedesign the journey around what agents make possible, rather than automating the journey you already have.
• 360-degree agentic customer supportResolution across chat, voice, email, and messaging, with shared context on every channel.
• Agentic customer data lakesA unified customer view that agents can act on.
• AI-native e-commerceConversational and agentic commerce across chat, voice, email, and messaging.
• Legacy contact-center and CRM modernizationSystems that resolve autonomously in place of infrastructure that routes tickets.
• Voice agent design and developmentNatural, low-latency voice agents built for real conversations, not phone trees.

Faster resolution, lower cost to serve, and an experience that improves as it scales.