From AI curiosity to measurable outcomes
We help mid-market and enterprise teams adopt AI the way it actually works in production: a clear strategy, a focused first pilot, the right model for the job, and the governance to scale it safely.
Free 30-minute discovery call. No slide decks — working session with a senior engineer.
What clients see
- From assessment to first pilot in production
- 4–8 wks
- Reduction in time spent on automatable knowledge work
- 60–80%
- Faster iteration vs. internal-only AI build-outs
- 3–5x
- Customer-owned IP, models, and prompts
- 100%
From assessment to first pilot in production
Reduction in time spent on automatable knowledge work
Faster iteration vs. internal-only AI build-outs
Customer-owned IP, models, and prompts
What we deliver
A focused set of capabilities, applied to the use cases where AI actually pays for itself — not a buzzword tour.
AI Readiness & Strategy
Maturity assessment across data, infrastructure, talent, and governance. We translate AI ambition into a sequenced roadmap with budget, risk, and ROI clearly mapped.
- AI maturity & opportunity audit
- Use case discovery and prioritization
- Business case and ROI modeling
- Build vs. buy vs. partner recommendation
LLM & Generative AI Integration
Production integrations with Claude, GPT, Gemini, and open-source models. We pick the right model for each task, not the one with the loudest demo.
- Model selection and evaluation
- Prompt engineering and guardrails
- Streaming, caching, and cost controls
- Multi-model routing and fallbacks
RAG & Knowledge Systems
Retrieval-augmented generation that grounds answers in your own data — with evaluation harnesses so quality doesn’t silently regress.
- Document ingestion and chunking
- Hybrid semantic + keyword retrieval
- Source citations and explainability
- Continuous evaluation pipelines
AI Agents & Workflows
Autonomous and human-in-the-loop agents that plan, call tools, and update systems of record — wired into the workflows your team already uses.
- Tool-using agents (MCP, function calling)
- Multi-step workflow orchestration
- Approval gates and audit trails
- CRM, ERP, and ticketing integrations
Custom Model & ML Development
When off-the-shelf models aren’t enough — fine-tuning, distillation, and classical ML for tasks where accuracy, latency, or cost demands a custom approach.
- Fine-tuning and LoRA adapters
- Embedding model training
- Classification, ranking, and forecasting
- On-prem and VPC deployments
AI Governance & Responsible AI
Policies, controls, and monitoring that satisfy security, legal, and compliance — without slowing the team down.
- Risk register and policy templates
- PII redaction and data residency
- Bias, hallucination, and toxicity monitoring
- SOC 2, GDPR, HIPAA-aligned controls
Four ways to work with us
Most clients start with an assessment or a pilot. From there, we scale into build or operate engagements as confidence and ROI compound.
AI Readiness Assessment
2–3 weeks
A structured audit of your data, systems, and team — ending in a prioritized AI roadmap.
Deliverables
- AI maturity scorecard
- Ranked use case backlog
- Build/buy recommendation
- Budget and timeline plan
AI Pilot Sprint
4–6 weeks
Ship a single high-ROI AI use case end-to-end in production with measurable success criteria.
Deliverables
- Working pilot in production
- Evaluation harness and metrics
- Operational runbook
- Go/no-go scaling decision
AI Build & Integrate
2–6 months
Design and deliver a production AI system — agents, RAG, or custom models — integrated with your stack.
Deliverables
- Production architecture
- Source code and IP transfer
- CI/CD and observability
- Team enablement and handoff
AI Operate & Improve
Ongoing
Day-2 operations: monitoring quality, controlling cost, and shipping new capabilities on a steady cadence.
Deliverables
- Quality and cost dashboards
- Continuous eval pipelines
- Quarterly roadmap reviews
- On-call coverage for AI systems
A pragmatic adoption path
Each step has a clear deliverable and a go/no-go decision. You can stop, scale, or pivot after any phase — without losing what was built.
- 01
Discovery call
Free 30-minute working session to understand goals, constraints, and current AI investments.
- 02
Readiness assessment
We audit data, infrastructure, governance posture, and team capabilities, then rank use cases by impact and feasibility.
- 03
Pilot design
Pick the smallest end-to-end slice that proves business value. Define success criteria and evaluation metrics up front.
- 04
Build & evaluate
Iterative delivery with weekly demos, an evaluation harness for quality regressions, and cost telemetry from day one.
- 05
Production rollout
Security review, governance sign-off, monitoring, and a phased rollout — not a big-bang launch.
- 06
Operate & scale
Continuous evaluation, model updates, new use cases on the same platform, and quarterly business reviews.
Where we've put AI to work
Domain context shapes the right use cases, the right model, and the right governance. Our engagements ship faster because we've seen the failure modes before.
Healthcare
Clinical intake, prior auth, and HIPAA-aligned LLM workflows.
Finance
KYC, statement parsing, fraud signals, and audit-ready agent workflows.
Insurance
Claims triage, FNOL automation, and fraud detection.
Legal
Contract review, clause extraction, and matter intake.
Retail & E-commerce
Catalog enrichment, support deflection, and merchandising agents.
Education
Curriculum assistants, grading support, and student services agents.
Public Sector
Constituent services, FOIA processing, and policy summarization.
Professional Services
Knowledge management, proposal generation, and billable-hour reclamation.
Common questions about AI adoption
Answers to the questions we get on most discovery calls. If yours isn't here, ask us directly.
How is AI consulting different from generic automation consulting?
Automation consulting tends to focus on deterministic, rules-based workflows. AI consulting deals with probabilistic systems: evaluation, hallucination management, model selection, cost controls, and governance. We do both, but we treat AI projects with the additional rigor they require — including evaluation harnesses and quality monitoring from day one.
We are not sure where to start. Can you help us pick the right AI use case?
Yes. Most engagements begin with a 2–3 week AI Readiness Assessment that produces a ranked backlog of use cases scored by business impact, feasibility, data readiness, and risk. The output is a sequenced roadmap, not a slide deck.
Do you build with proprietary models, open-source models, or both?
Both. We select the right model for each task based on quality, latency, cost, data residency, and governance constraints. Common stacks include Claude, GPT, Gemini, and open-source models like Llama or Mistral, often deployed in your own VPC.
How do you handle data privacy, residency, and compliance?
We design to your security and compliance posture — SOC 2, GDPR, HIPAA, and industry-specific regulations. Options include private VPC deployments, on-premise inference, PII redaction at ingest, and contractual zero-retention agreements with model providers.
Who owns the models, prompts, and code we build together?
You do. We transfer full IP — source code, prompts, evaluation datasets, and fine-tuned weights — at project completion. You are never locked into our team or our infrastructure.
How quickly can we see results?
A focused AI Pilot Sprint typically delivers a working production pilot in 4–6 weeks. Larger build engagements ship value in phases so business stakeholders see results within the first month.
Can you augment our internal AI team instead of replacing it?
Yes. A large share of our work is alongside internal data, ML, and platform teams — covering specific gaps like LLM integration, evaluation infrastructure, or governance design while your team retains ownership of the broader roadmap.
What does AI governance actually look like in practice?
Concretely: a policy document teams can follow, an approved-models list, an inventory of AI use cases, PII redaction at data boundaries, evaluation pipelines for quality and safety, an incident-response playbook, and dashboards for cost, quality, and usage.
Ready to make AI real for your team?
Book a 30-minute working session with a senior engineer. We'll map your highest-ROI AI use cases and outline the smallest path to production.