Unlock the transformative potential of AI and machine learning.

AI and ML services are the design, build, deployment, and ongoing operation of artificial intelligence systems inside real businesses. Practically, this means: (1) LLM applications — chat assistants, document summarization, structured data extraction, content generation, code copilots; (2) Retrieval-Augmented Generation (RAG) systems that ground model answers in your private data; (3) autonomous AI agents that plan, use tools, and complete multi-step workflows; (4) computer vision for document processing, defect detection, and medical imaging; (5) predictive ML for forecasting, churn, fraud, and pricing; and (6) the MLOps infrastructure — model registry, evaluation harness, drift monitoring, feature store, retraining pipelines — that keeps these systems accurate in production. RunWhatMatters delivers all six disciplines under one Boston-based studio, with engineers who have shipped AI products to regulated industries including healthcare, finance, and the federal government.

AI engagements at RunWhatMatters follow three pricing tiers. Strategy sprints run $25K–$75K for 2–4 weeks of AI use case prioritization, data audit, model selection, and ROI modeling — the deliverable is a written AI roadmap, not a demo. Proofs of concept run $75K–$250K over 6–12 weeks and ship a working RAG or copilot against your real data, with an eval suite that proves quality. Production AI platforms start at $250K and scale to $2M+ for enterprise-grade systems with full MLOps, custom fine-tuning, multi-model orchestration, and EU AI Act compliance. Most engagements combine fixed-fee sprints for well-scoped work with time-and-materials for exploratory phases. Every engagement begins with a written estimate, a no-surprise SOW, and an honest conversation about what AI can and cannot do for your business.

The right answer depends on four variables: data sensitivity, latency budget, cost target, and capability requirements. OpenAI (GPT-4o, o1, o3) leads on raw capability, tool use, and the longest production track record — best for complex reasoning, structured outputs, and code generation. Anthropic (Claude 3.5 Sonnet, Claude 3 Opus) leads on long-context (200K tokens), code quality, and instruction-following nuance — best for legal/medical text, agentic workflows, and customer-facing assistants where tone matters. Open-source (Llama 3.1, Mistral, Qwen 2.5, Mixtral) wins for data sovereignty, predictable cost at scale, and custom fine-tuning on your domain. RunWhatMatters builds model-agnostic architectures with abstractions that let you swap providers in days, not months — and we routinely run A/B evals against 3–4 models before recommending the primary. We have production deployments on every major provider and on private cloud (AWS Bedrock, Azure OpenAI, vLLM, NVIDIA NIM).

RunWhatMatters builds AI governance in from line one of the engagement, not bolted on at audit time. The deliverables are concrete: Model cards for every model in production, documenting training data, intended use, limitations, and eval results; Data lineage tracking every input from source system to model prompt; Bias and fairness testing across protected categories with documented mitigation; Drift monitoring with statistical alerts when input distributions or output quality shift; Human-in-the-loop controls for high-stakes decisions; Audit trails for every model call, prompt, and response. For EU AI Act compliance, RunWhatMatters has prepared clients for high-risk system classification (Annex III) including risk management (Art. 9), data governance (Art. 10), technical documentation (Art. 11), record-keeping (Art. 12), transparency (Art. 13), human oversight (Art. 14), and post-market monitoring (Art. 72). We have also delivered model risk management frameworks aligned to SR 11-7 for US financial services and NIST AI RMF for federal agencies. Every AI system we ship includes a deployable governance package, not a PDF.

Yes — and we do it with the safety controls that make autonomous systems trustworthy in production. RunWhatMatters has shipped multi-agent systems for customer support triage, sales prospecting, clinical documentation, code review, and supply chain optimization. Our agent architecture follows a layered pattern: Orchestration (LangGraph, CrewAI, or custom) managing planning loops and state; Tool use with strict schemas, allow-lists, and per-tool rate limits; Memory split between short-term (conversation), long-term (vector store), and episodic (task history); Guardrails including input validation, output filtering, hallucination detection, and PII redaction; Human-in-the-loop checkpoints at configurable confidence thresholds; Observability with full traces, tool-call logs, and replay capability. We treat agent systems as production software, not demos — versioned, tested, monitored, and kill-switchable. The difference between our agent work and the average AI agent startup is that our agents run in production at clients where a bad action costs real money, real customers, or a regulatory finding. We have earned the scar tissue.

Typical RunWhatMatters AI engagement timelines. Strategy sprint — 2 to 4 weeks, delivers a written AI roadmap and prioritized use case portfolio. Proof of concept — 6 to 12 weeks, delivers a working RAG system or copilot with eval suite, integrated with one source system. Production MVP — 4 to 6 months, delivers a GA-ready AI feature with MLOps, monitoring, governance, and a defined SLO. Enterprise AI platform — 6 to 18 months, delivers the full platform with multi-model orchestration, fine-tuning pipelines, custom evals, and the governance package. The bottleneck is almost never the model — it is data access, integration complexity, and the eval harness that proves quality. RunWhatMatters plans for all three upfront.