Artificial intelligence, engineered for production.
We help organizations move AI beyond demos and pilots into governed, measurable, revenue-relevant systems — with the strategy, engineering and controls to keep them running.
Executive summary
AI is now a board-level topic in almost every industry, and most enterprises are still working out how to translate that attention into results. The gap is rarely the model — it is the surrounding system: strategy, data, workflow integration, adoption, controls.
Laminin's AI capability spans that full surface. We work with leadership to frame where AI creates real value, design the architectures that make it feasible, build the systems that put it into daily use, and stand up the governance that keeps it defensible.
We serve financial services, healthcare, technology, consumer, real estate and public-sector organizations where the cost of getting AI wrong is high — and where the payoff of getting it right is material.
The problems we address
A portfolio of AI pilots that never reach production
Interesting demos, unclear value, no path to integration with the systems and processes people actually use.
Generative AI adoption without controls
Teams using foundation models in ways the organization cannot see, measure, secure or defend to a regulator.
Data unfit for the AI ambition
Strategy assumes clean, integrated, well-governed data. Reality is fragmented sources, uneven quality, and no lineage.
Uncertain economics and unclear ROI
Model, infrastructure and operating costs that are hard to forecast, and value cases built on qualitative claims.
Regulatory, ethical and reputational exposure
Emerging AI regulation, sector-specific obligations, and internal principles that are not yet operationalized.
Workforce and operating-model impact
Uncertainty about which roles change, which processes are redesigned, and how to bring people through the transition.
What Laminin does
AI strategy and value cases
Enterprise-wide opportunity assessments, prioritization frameworks, and board-ready roadmaps with defensible economics.
Generative AI and agent systems
Design and delivery of retrieval-augmented systems, autonomous and human-in-the-loop agents, and orchestration architectures.
Machine learning and predictive systems
End-to-end ML for forecasting, risk, personalization, decision support and operations — from feature engineering to MLOps.
AI infrastructure and platforms
Model access layers, vector and knowledge stores, evaluation harnesses, and cost/observability tooling for enterprise scale.
Responsible AI and governance
Operationalized principles: risk tiers, review gates, model cards, evaluation policies and audit trails.
AI readiness and enablement
Operating-model design, capability build-out, executive education, and internal centers of excellence.
A structured taxonomy for AI work
Twelve disciplines that combine into the AI programs our clients run.
AI Strategy
Where AI creates measurable value and how to sequence the investment.
Generative AI
Foundation-model applications built around retrieval, tools and controls.
Enterprise AI
AI embedded in core operations — customer, employee and back-office systems.
AI Agents
Orchestrated, tool-using agents with clear scopes, oversight and escalation.
Intelligent Automation
Process automation augmented by ML and generative models where they earn their place.
Machine Learning
Classical and deep ML for prediction, ranking, classification and optimization.
Knowledge Systems
Enterprise knowledge stores, retrieval architectures, ontologies and taxonomies.
AI Product Development
Discovery, design and engineering for AI-first products and features.
AI Infrastructure
Model gateways, vector stores, evaluation and observability, cost management.
Responsible AI
Fairness, safety, transparency and human oversight, translated into engineering practice.
AI Governance
Policies, review gates, accountability, documentation and regulatory alignment.
AI Readiness
Operating model, org design, talent, adoption and change through the AI transition.
Four phases from ambition to operation
Frame
Clarify the strategic thesis, prioritize opportunities, and build a defensible value case with explicit assumptions.
Design
Architect the system end-to-end: data, models, retrieval, orchestration, evaluation, controls, and human workflow.
Build
Engineer in production-grade environments with evaluation harnesses, observability and rollback from day one.
Operate
Governance, monitoring, cost management, model refresh and continuous improvement — the part that determines whether AI stays.
The stack we work in
Concrete building blocks — chosen per engagement, not prescribed as a house stack.
- Foundation modelsFrontier and open-weight models across families (GPT, Claude, Gemini, Llama, Mistral, DeepSeek and successors), selected by capability, cost, deployment mode and data-residency constraints.
- RetrievalRetrieval-augmented generation with hybrid search (dense + sparse), reranking, chunking strategies tuned to the corpus, and evaluation on grounded answer quality.
- AgentsTool-using agents with explicit scopes, structured tool contracts, guardrails, and human-in-the-loop for high-consequence actions.
- Vector & graphVector stores (Pinecone, pgvector, Weaviate, Milvus and equivalents) and knowledge graphs where relationships carry the signal.
- MLOpsFeature stores, experiment tracking, model registries, CI/CD for models, and monitoring for drift, quality and cost.
- EvaluationTask-specific eval sets, LLM-as-judge with human calibration, red-teaming, and continuous evaluation in production.
- GovernanceRisk tiering, model cards, use-case registers, review gates, and controls aligned to NIST AI RMF and emerging regulatory frameworks.
Related industries, work and insights
Relevant industries
Related work
Questions executives ask us first
How do you decide where AI actually belongs?
We start from the value equation: what changes economically, operationally or competitively if this problem is solved. Where the answer is qualitative or marginal, we recommend against AI. Where it is quantifiable and material, we design for it.
Should we build, buy or fine-tune?
Most enterprises should compose. Foundation-model APIs for general capability, retrieval and orchestration for domain fit, fine-tuning where a well-scoped task justifies it, and in-house model work only where differentiation demands it.
How do you handle governance and risk?
Governance is designed alongside the system, not bolted on. Risk tiering per use case, evaluation policies with acceptance criteria, human-in-the-loop for high-consequence actions, and documentation that survives an audit.
What does an engagement look like?
Ranges from a four-to-six-week strategy sprint to multi-quarter build-and-operate programs. Each is shaped around a defined outcome — never staffing hours against ambiguity.
Do you work alongside our existing AI or data teams?
Frequently. We are comfortable as lead, as an accelerator inside an existing team, or as an independent evaluator of work already in flight.
Ready to make AI a system, not a slide?
Bring us the problem you're trying to solve. We'll shape the AI approach that fits it.