Should we use a frontier API or an open-weight model?

It depends on data sensitivity, cost at your volume, and how much control you need over behavior. We usually validate value fast on a hosted frontier model, then benchmark open-weight options once traffic patterns are clear. Mature systems often route: a small model handles easy cases, a large one handles the rest.

Our data is messy and scattered. Can we still start?

Yes, and that is the normal starting point. Retrieval quality depends far more on structure and metadata than on raw volume, so we scope one narrow domain, fix parsing and chunking for that slice, and expand only when evaluation scores hold.

How do you prevent hallucinations?

We constrain answers to retrieved context, require citations, add explicit refusal paths when confidence is low, and re-run evaluations on every change. For high-stakes flows we keep a human approval step rather than pretending a model is infallible.

Who owns the models, prompts, and code?

You do. We work in your repositories and cloud accounts and hand over prompts, evaluation sets, pipelines, and infrastructure code with documentation. There is no runtime dependency on us.

Can you work alongside our existing data or ML team?

Often that is the best setup. We embed with your engineers, cover the gaps that slow teams down most, usually retrieval, evaluation, serving, and MLOps, and transfer the practices so momentum survives the engagement.

AI & Machine Learning

We build the full AI stack around your data: retrieval and copilots grounded in your private knowledge, agentic workflows that complete real tasks, and classical models for forecasting, scoring, and anomaly detection. Every system ships with evaluation suites, tracing, and a token and latency budget, so it holds up in front of real users instead of only in the demo.

Start a project

Most AI projects fail in production, not in the demo

A prototype that answers five hand-picked questions is easy. A system that answers ten thousand real ones, cites its sources, refuses when it should, and costs what you budgeted is engineering work.

We build that system: retrieval grounded in your own data, agents that call your APIs under real business rules, and classical models where a language model is the wrong tool. Evaluation, tracing, and cost controls go in from the first sprint, not after the first incident.

2 wks

Discovery to a scoped, ranked AI roadmap

6–10 wks

Prototype to a production-ready first release

Eval-first

Every model change gated by regression suites

Your cloud

Runs in your accounts, your data stays yours

What's included

Generative AI products, LLM agents, and production ML, shipped with evaluation, guardrails, and cost control from day one.

Generative AI products, copilots & assistants

Agentic workflows, tool calling & orchestration

RAG over private data with hybrid search & reranking

Fine-tuning, distillation & open-weight model hosting

Predictive ML: forecasting, scoring & anomaly detection

MLOps, evaluation harnesses & drift monitoring

Four things separate an AI feature from an AI product

Every engagement is assembled from these four layers. Skip one and you get the demo that never quite ships.

Ground it in your data

Hybrid retrieval across documents, tickets, and warehouse tables, with chunking, reranking, and citations so every answer traces back to a source a human can open.

Let it reason and act

Agents that call your APIs, respect your business rules, and hand off to a person when confidence drops, rather than chat that only sounds convincing.

Prove it works

Golden datasets, regression suites, and model-graded scoring wired into CI, so a prompt tweak or model upgrade can never silently degrade quality.

Run it economically

Model routing, caching, quantization, and streaming tuned against a token, latency, and GPU budget you approved before launch.

Generative AI and machine learning, applied to real workflows

We start from the workflow that costs you money or time, then choose the model class that fits, from a frontier LLM to a gradient-boosted tree.

Enterprise copilots & assistants

Assistants that work over your private knowledge with permissions intact, so support, sales, and internal teams stop hunting through wikis and PDFs.

Grounded answers with source citations
Role-aware access control
Deflection and satisfaction tracking

Agentic workflow automation

Multi-step agents that read a request, gather context, call your systems, and complete the task, with approvals wherever the stakes justify a human.

Tool and API orchestration
Human-in-the-loop approvals
Deterministic fallback paths

Document & multimodal intelligence

Contracts, claims, invoices, images, and call recordings turned into structured, validated records your existing systems can consume.

Extraction with confidence scoring
Vision-language and OCR pipelines
Speech to structured data

Predictive & decision models

Forecasting, scoring, and anomaly detection where accuracy and explainability matter more than fluency, deployed behind clean APIs.

Demand, capacity and revenue forecasting
Churn, risk and pricing models
Anomaly and fraud detection

Model platform & MLOps

The unglamorous layer that keeps AI alive: pipelines, registries, evaluation gates, and dashboards for quality, drift, and spend.

Feature and vector store design
Model registry with CI/CD gates
Drift, quality and cost dashboards

Private & open-weight deployment

For regulated data or high volume, self-hosted models tuned on your examples and served inside your network with no data leaving it.

Self-hosted open-weight models
Fine-tuning and distillation
VPC-only inference, no data egress

From use-case triage to a system your team can run

A typical first engagement runs eight to twelve weeks. Each stage ends with something reviewable, so you can stop or expand on evidence.

Stage 01

1–2 weeks

Opportunity mapping

We audit your data, workflows, and constraints, then rank candidate use cases by value, feasibility, and risk, instead of starting from whichever model is trending.

Use-case scorecard
Data readiness review
Target architecture sketch

Stage 02

2–3 weeks

Grounded prototype

A working slice on your real data with retrieval, prompts, and a first evaluation set, so stakeholders judge quality on evidence rather than a scripted demo.

Clickable prototype
Golden evaluation dataset
Cost and latency baseline

Stage 03

3–5 weeks

Hardening & guardrails

Permissions, PII handling, refusal behavior, fallbacks, and regression suites running in CI, plus load testing on the paths that actually carry traffic.

Guardrail and safety layer
Regression suite in CI
Tracing and observability

Stage 04

Ongoing

Launch & improve

Staged rollout with monitoring on quality, cost, and drift, and a change process your engineers can run, including prompt updates and retraining.

Staged rollout plan
Quality and spend dashboards
Runbooks and team enablement

Outcomes we optimize for

Answers users trust, with citations and predictable failure behavior

Hours of manual review, triage, and data entry reclaimed

Token, GPU, and latency budgets that hold as usage grows

An in-house team confident enough to ship AI changes without us

What we build it with

The tools we reach for on AI & ML work, picked for the problem in front of us and for the team who inherits the code.

Modelling

Where the training and fine-tuning happens

Python
PyTorch
Hugging Face
scikit-learn

LLM and agents

Orchestration across models and tools

LangGraph
LangChain
OpenAI
Anthropic
Ollama

Retrieval and serving

Grounding answers, then serving them fast

FastAPI
vLLM
Qdrant
PostgreSQL
Ray

Evaluation and ops

Proof it still works after the next model swap

MLflow
Weights & Biases
Docker
AWS

Questions we get in the first call

It depends on data sensitivity, cost at your volume, and how much control you need over behavior. We usually validate value fast on a hosted frontier model, then benchmark open-weight options once traffic patterns are clear. Mature systems often route: a small model handles easy cases, a large one handles the rest.

Explore more

Let's build something that lasts

Tell us about your project and we'll get back to you within 12 hours with next steps.

Start a project See our work