Capabilities

Deep coverage across the AI stack.

Whether you are shipping a customer-facing copilot, hardening an internal automation, or scaling a model-backed service, we place people who have done it before—across modern AI and the platform work that makes it dependable. AI is our focus, but most engagements also need excellent backend, full-stack, and design craft. We cover that stack too.

Modeling: LLM apps, agents, classical ML, computer vision, NLP—shipped with eval harnesses, not vibes.
Platform: Data foundations, MLOps, backend services, and cloud-native infra that keeps models honest in production.
Product: Full-stack engineering and senior product design, so AI features feel intentional inside real applications.

What we ship

Capabilities, not job titles.

Each card below is a body of work we have shipped multiple times. Engagements usually combine two or three of them—rarely just one in isolation.

LLM / GenAI

Large language models & agents

RAG, tool use, evaluation harnesses, guardrails, latency/cost tuning, and production prompts—not brittle prototypes.

MLOps

MLOps & reliable ML systems

Training and serving pipelines, observability, feature stores, drift monitoring, and safe release practices for models in the wild.

Data

Data platforms & analytics engineering

Warehouse/lake design, streaming ingestion, dbt-style transformations, and metrics layers that AI products can trust.

Vision

Computer vision & multimodal

Detection, segmentation, OCR, embeddings, and multimodal retrieval for product features and operations automation.

NLP

NLP & information extraction

Classification, NER, summarization, semantic search, and document AI integrated into your workflows.

R&D

Applied research → product

Feasibility spikes, benchmarking, and pragmatic roadmaps that turn research questions into shippable milestones.

Platform

Backend & platform engineering

APIs, event-driven services, cloud-native infra, and security-minded foundations for AI-heavy applications.

Product

Product engineering with AI

Full-stack squads that pair UX clarity with model behavior—so features feel intentional, not experimental.

Build & design

Full-stack, frontend, backend & product design

Modern UIs, APIs, services, and systems design—so AI ships inside real products. We place senior engineers and designers who own delivery end-to-end, not only model code.

Engagement shapes

Six common shapes of work.

Most projects we take on resolve to one of these patterns or a combination of them. The shape sets the team mix, the timeline, and what production-ready means.

Customer-facing AI copilot

Chat, search, or assistive UX backed by retrieval, evaluation, and cost-aware orchestration—designed to live in your product, not as a side demo.

Retrieval
Eval harness
Latency budget

Internal automation & agents

Workflow agents that handle real operations work—triage, classification, drafting—with audit trails and human review where it matters.

Tool use
Audit trail
Approval flows

Retrieval over your data (RAG)

Ingestion, chunking, hybrid search, and freshness pipelines tuned for your corpus—plus the evaluation that proves it actually answers the question.

Ingestion
Hybrid search
Freshness

Evaluation & guardrail harness

Offline and online evaluation suites, regression gates, safety filters, and red-team batteries that catch quality drops before users do.

Offline evals
Online metrics
Regression gates

ML platform & MLOps from scratch

Training and serving stacks, model registry, feature store, observability—the foundations that turn one-off models into reliable releases.

Serving
Registry
Observability

Data foundation for AI

Streaming ingest, warehouse and lake design, semantic and metrics layers—the backbone modern ML systems quietly depend on.

Streaming
Warehouse
Semantic layer

Quality bars

What "production-ready" means here.

Capabilities are table stakes. The bar that separates serious AI work from theatre is the posture engineers bring to every release. These four are non-negotiable on our side.

Eval-first

Every release ships with offline metrics and online signal. Regressions surface in CI, not in user complaints.
Observability from day one

Traces, token costs, p95 latencies, and eval drift are wired in alongside the first prototype—not added after launch.
Cost & latency budgets

Explicit per-request token, dollar, and millisecond targets. Models earn their price tag or they don't ship.
Security & compliance posture

Least-privilege data access, PII handling, audit logging, and patterns that hold up to SOC 2 / GDPR review.

Where we draw the line

Honest about what we don't take on.

The fastest way to a bad engagement is to pretend everything is in scope. Saying no early is part of the service.

PoCs with no production path

We focus on work that intends to reach users. Pure experiments without a path to ship are better served by a research consultancy.
"AI everywhere" without a business case

We will help you find one—but we won't manufacture it. Capability without a problem to solve is theatre.
Black-box deliverables

Code lives in your repository. Decisions live in writing. We don't lock you into our stack or our tooling.
Junior-only staffing

Every engagement has senior accountability. We do not pad proposals with juniors carrying titles they have not earned.

Have a shape that doesn't fit cleanly into any of these? Tell us the problem and the constraints—we will tell you honestly whether we are the right team for it.

Talk to us about scope