AI Readiness & Workflow Discovery (weeks, not months)

• Hands-on workflow mapping and systems assessment
• Feasibility scoring (ROI, integration complexity, risk)
• Working prototypes during discovery
• Clear recommendation on what to build first

Data & Systems Unification for AI

• Unify fragmented data across enterprise systems (CRM, ERP, document stores, internal APIs)
• Vector databases, embedding pipelines, and retrieval systems tuned for production accuracy
• Build clean pipelines and standardized schemas
• Secure, permissioned interfaces to internal systems
• Production-grade integrations

Targeted Agentic Workflows

• Customer-facing agents: support triage, sales qualification, voice automation
• Internal ops: routing, approvals, compliance drafting, document review
• Knowledge copilots, scoped to a team or role
• Engineering workflow automation (QA, CI/CD, release gating)
• Built with the human-in-the-loop patterns that make agents safe to deploy

Modernization for the AI Era

• Most AI projects fail because the underlying systems aren't ready. We fix that first.
• Untangle legacy systems, brittle integrations, and undocumented APIs
• Replace one-off scripts with the durable infrastructure agents need to run safely
• Build the observability and rollback layer most enterprises don't realize they need
• Bring code, data, and infra to the bar AI workloads actually require

Reliability, Evals & Guardrails

• Eval harnesses that catch regressions before they reach production
• Confidence scoring so users know when to trust the output and when to verify
• Permissions and audit logs that pass enterprise security review
• Monitoring, rollbacks, and incident responses
• Accuracy framing

Change Management & Adoption

• Phased rollout: pilot, expand, hand off
• Operator-in-the-loop interfaces so users learn the system as they use it
• Live dashboards showing what AI is doing, what it's saving, where it's wrong
• Internal handoff to your team, with documentation engineers will actually read
• Success measurement by adoption and ROI

Agentic Product Development

• Greenfield agent products from spec to launch
• Multi-step workflows with tool use, function calling, and stateful memory
• Human-in-the-loop where it matters
• Founder-stage iteration speed: ship, talk to users, iterate

AI UX & Product Design for Agents

• Interaction patterns that handle failure gracefully (retries, fallbacks, escape hatches)
• Confirmations and previews that build trust without slowing things down
• Visualizing what the agent is doing, not just what it returned
• Designs that survive the gap between what the model can do today and in future

Retrieval & Knowledge Systems

• RAG pipelines (ingestion → retrieval → eval)
• Document intelligence: parsing, OCR, semantic extraction
• Embedding strategy and vector store selection (Pinecone, Weaviate, pgvector)
• Hybrid search and reranking when accuracy matters more than speed

Model & Systems Engineering

• Model selection across labs (Claude, GPT, Gemini, Llama) tuned for cost, latency, and accuracy
• Fine-tuning and adapters where it adds value
• Prompt engineering, routing, and policy layers
• Multi-model architectures: cheap models for the easy 80%, frontier models for the hard 20%

Production Hardening

• Observability and evals
• Safe deploys, release gating, rollbacks
• Safety, compliance, and cost monitoring

Internal AI Tooling

• Admin dashboards and analytics
• Feedback loops and replay systems
• Labeling and evaluation harnesses

Embedded Senior Pods

• 1-5 senior cross-functional forward-deployed engineers, no juniors
• Fully integrated into standups, sprints, code reviews, and on-call
• Engineers who are product-minded, handle ambiguity, diagnose problems and make architectural calls in the room - not just write code to a spec
• Engineers who've built and shipped production systems and owned outcomes

Customer Implementation for Platforms

• Embed alongside your enterprise customers to land integrations, configurations, and production launches
• Field-tested playbooks from pilot through full-scale rollout
• Feedback loop from the field back to your product team (what's working, what's missing, what's about to break)
• Higher activation, faster time-to-value, lower churn
• Support pre-sale and post-sale motions and expansion

AI Infrastructure & MLOps

• Eval frameworks, monitoring, and observability built on production stacks (W&B, Braintrust, LangSmith, etc)
• Data pipelines that can withstand handle messy enterprise inputs
• Cost optimization across model choice, prompt design, and routing
• Reliability hardening
• Stabilizing brittle systems and infra so AI workloads can run on top

Pattern Library from Cross-Industry Engagements

• Insights from 40+ AI engagements across finance, healthcare, retail, logistics, and CPG
• Architectural patterns we've seen work, hold at scale, and fail
• Identification of cross-vertical edge cases and performance bottlenecks

Acceleration When You Need It

• Unblock stalled roadmaps without distracting your core team
• Ship new workflows and integrations in weeks, not quarters
• Reduce time-to-production for customers
• Scale customer support work that would otherwise require permanent headcount