AI & Agentic Systems
Enterprise AI that ships, and pays back.
We design, build and operate production AI (agentic workflows, generative AI assistants and machine learning) on governed data, with evaluation, security and cost controls engineered in from day one.
Why it matters
The model is the easy part. Production is the hard part.
Foundation models have made impressive demos trivial. Getting AI into production, where it touches customers, money or regulated data, is a different discipline entirely: grounding answers in trustworthy data, integrating with systems of record, measuring quality continuously, controlling cost per transaction, and satisfying security, risk and compliance teams.
That's where most enterprise AI initiatives stall. We focus on that gap. Our engagements start from a business KPI (handle time, cycle time, loss ratio, conversion, cost to serve) and work backward to the data, architecture and controls needed to move it.
What we build
- Agentic AI: multi-step agents that use tools and APIs to complete work, with approvals and audit trails.
- Generative AI & RAG: assistants and search grounded in your documents and data, with citations.
- Machine learning: forecasting, scoring, anomaly detection and optimization with MLOps.
- AI platforms: shared gateways, prompt and model management, evaluation and observability so every new use case is faster than the last.
Solutions in this practice
Start with the problem you have
Each solution can run on its own or as part of a broader program.
What we deliver
Capabilities
Delivered by senior architects and specialist engineers, with security and governance designed in.
Agentic workflow design
Process mapping, tool design, autonomy levels, approval steps and exception handling for agents that do real work.
RAG & knowledge systems
Retrieval pipelines, chunking, hybrid search, re-ranking and citations over documents, tickets and databases.
LLM application engineering
Model selection, prompt and context engineering, fine-tuning where warranted, and multi-model routing.
Evaluation & LLMOps
Golden datasets, automated evals, red-teaming, monitoring for drift, hallucination and cost per task.
Security & guardrails
PII protection, prompt-injection defenses, access controls, audit logging and policy enforcement.
AI value measurement
Baselines, A/B and holdout designs, and dashboards that tie AI to business KPIs.
Use cases
Where it pays off
- Customer serviceAgent assist, case summarization, next-best action and self-service that resolves, not deflects.
- Operations & back officeInvoice, claims and document processing; reconciliation; exception handling.
- Sales & marketingAccount research, proposal drafting, content operations with brand and compliance checks.
- IT & engineeringTicket triage, incident summarization, code modernization and test generation.
- Risk & compliancePolicy Q&A, control testing support, regulatory change analysis with citations.
- Knowledge workEnterprise search and assistants grounded in your SharePoint, Confluence, CRM and data warehouse.
Approach
How the engagement runs
Clear phases, clear exit criteria, and a working result at every stage.
Assess
Value-sized use cases, data and platform readiness, risk assessment and a prioritized roadmap.
Prove
A working pilot on real data with an evaluation suite, cost per task and security review.
Productionize
Integrations, guardrails, observability, human-in-the-loop design and change management.
Scale & operate
Shared AI platform components, new use cases and managed operations with SLAs.
Typical starting engagement
AI Pilot-to-Production
Fixed scope, fixed fee, and a clear decision at the end. Final pricing is confirmed after a short scoping call.
Scope this engagement →Starting at
- One high-value use case, value-sized and approved
- Working solution on real data with integrations
- Evaluation suite, guardrails and cost model
- Security and risk review package
- Production deployment and handover runbook
FAQ
Frequently asked questions
Straight answers to the questions CIOs, CTOs and procurement ask us first.
How do you choose the first AI use case?
We score candidates on value, feasibility, data readiness, risk and time to impact, then pick one that can show measurable results in about 12 weeks and builds reusable foundations for the next ones.
Which models and platforms do you use?
We're model-agnostic: OpenAI, Anthropic, Google Gemini, Meta Llama and others, via Azure, AWS Bedrock, Google Vertex AI or self-hosted where data or cost requires it. We design for portability so you're not locked to one provider.
How do you prevent hallucinations?
By grounding responses in retrieved, permissioned data with citations; constraining outputs; using automated evaluations against golden datasets; and adding human review where the risk warrants it. We measure accuracy continuously in production rather than assuming it.
Will our data be used to train public models?
No. We architect solutions using enterprise agreements and configurations that exclude your data from model training, with encryption, access controls and logging under your ownership.
How do you measure ROI?
Every use case has a baseline and target KPI agreed before build, a cost-per-task model, and a measurement design (A/B, holdout or before/after with controls) so finance can validate the result.
Start here
Get a senior architect's view of your fastest path to value.
A 45-minute working session, not a sales pitch. Bring your toughest AI, data or cloud problem; leave with a point of view, a rough plan and a realistic budget range.
- Senior architects onlyNo account managers in the first meeting.
- Fixed-fee optionsClear scope, price and outcomes up front.
- Mutual NDA on requestBefore any detailed discussion.