Services / Product to production

One team around the hard part.

Strategy, product design, AI engineering, data, and delivery stay connected so the system remains understandable after the prototype works.

01

Audits · Roadmaps · Feasibility

AI Strategy & Discovery

Turn an AI idea into a decision-ready product direction before expensive assumptions become architecture.

Useful when

Starting something new

You have a meaningful opportunity, but the user, workflow, data, model, and operating assumptions still need to become a coherent product decision.

Strengthening what already exists

You have a prototype, backlog, or product direction that needs an independent technical read, sharper acceptance criteria, or a credible route to production.

Core workstreams

  1. 01

    Opportunity framing

    Map the people, decisions, friction, and existing systems before choosing where AI belongs.

  2. 02

    Feasibility and risk

    Test the quality of available data, likely model behavior, provider constraints, cost, privacy, and failure modes.

  3. 03

    Prototype definition

    Define the smallest working slice that can answer the expensive questions without pretending to be the finished product.

Representative outcomes

  • A precise problem statement tied to a real user decision or workflow
  • A feasibility view covering data, model behavior, cost, safety, and operations
Related proofLloyd's Tire & Auto case studyOwned case studyLTTR case study
Explore AI Strategy & Discovery
02

RAG · Evals · Guardrails

LLM Applications & Agents

Build assistants and agentic systems that can use the right context, take bounded actions, and show when human judgment is required.

Useful when

Starting something new

You are shaping an assistant, copilot, agent, or generation product and need the interaction, context, actions, and safeguards designed as one system.

Strengthening what already exists

You have a chatbot or automation that demos well but lacks reliable grounding, evaluations, permissions, recovery, or human control.

Core workstreams

  1. 01

    Context and retrieval

    Design how the system finds, cites, refreshes, and limits the business knowledge used in each response.

  2. 02

    Tools and orchestration

    Connect models to APIs and workflows through bounded actions, typed inputs, and recoverable state.

  3. 03

    Evaluation systems

    Create representative test sets and measurable checks for quality, grounding, safety, latency, and cost.

Representative outcomes

  • A model and retrieval architecture chosen for the actual workflow
  • Bounded tools and actions with explicit permission and confirmation rules
Related proofbopGPT case studyPursuit Intelligence case studyLloyd's Tire & Auto case study
Explore LLM Applications & Agents
03

UX research · Design systems · Prototypes

Product & Interface Design

Make complex model behavior and operational state understandable enough for people to use with confidence.

Useful when

Starting something new

You need to turn a product concept into a coherent web or mobile experience with real workflows, edge cases, and a system the engineering team can build.

Strengthening what already exists

You have a working product whose interface has drifted, accumulated friction, or stopped making complex system behavior understandable.

Core workstreams

  1. 01

    Workflow design

    Translate user goals and operational constraints into complete task flows, not isolated screens.

  2. 02

    Interface systems

    Create responsive components, hierarchy, content patterns, and states that remain coherent as the product grows.

  3. 03

    AI interaction design

    Show sources, uncertainty, editable inputs, progress, approvals, and recovery around model behavior.

Representative outcomes

  • A clear information architecture built around user decisions
  • Responsive production interfaces for real data and edge states
Related proofOwned case studyMintproof case studyMipkin case study
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04

Pipelines · MLOps · Observability

Data, ML & Production Delivery

Create the data contracts, cloud infrastructure, monitoring, and operating boundaries that keep an intelligent product useful after launch.

Useful when

Starting something new

You need the data contracts, APIs, cloud architecture, security, and operating model that will let an intelligent product survive beyond its first demonstration.

Strengthening what already exists

You have a product constrained by fragile ingestion, unclear ownership, provider coupling, missing observability, or release processes that do not prove real readiness.

Core workstreams

  1. 01

    Data and catalog systems

    Build ingestion, normalization, identity, provenance, quality, and checkpoint systems around changing source data.

  2. 02

    Application infrastructure

    Design APIs, storage, queues, background work, caching, authentication, and cloud boundaries for the product.

  3. 03

    Release engineering

    Create repeatable builds, environment separation, migrations, health checks, rollback paths, and acceptance records.

Representative outcomes

  • Durable data contracts and resumable provider-aware pipelines
  • Secure application and API boundaries with explicit ownership
Related proofOrigin Peptides case studySetpoint case studyOwned case study
Explore Data, ML & Production Delivery

Stand alone. Work together.

A service can solve one constraint—or connect the whole path.

Discovery can define the right product bet. Product design can make a complex system understandable. LLM and agent engineering can connect context to bounded action. Data and production delivery can make the result reliable enough to own.

Some clients need one of those disciplines. Others need one accountable team to carry the decisions across all four.

Discuss the right starting point

Start a conversation

Bring us the problem that keeps surviving the meeting.

Tell us what you are trying to change, who it affects, and where the work stands today. We will give you an honest read on fit and the most useful next step.

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