Five kinds of engagement
Most start as one and grow into another. You can hire us for a single piece.
An AI-native platform to build on
We maintain a production AI agent platform and adapt it to your domain. It is multi-tenant and authenticated from the first commit, with long-term agent memory, sandboxed code execution, price tracking, feature flagging, and everything else you need to build a production-level application from the get-go.
It is deployed into your AWS account (or whatever other environment you want), running against your data, and you own the source at the end. Everything that normally consumes the opening months of an AI project already exists and is tested.
A live commercial product runs on this platform in production. The deployment path we hand you has already been through a release schedule.
Skills
Capabilities loaded on demand and authorized per user, so the agent's context stays small as the system grows.
Sandboxed execution
The agent runs Python in an isolated container with no network access.
Agent memory
Semantic recall across past conversations, on pgvector. The agent is not confined to the current thread.
Multi-tenancy
Organization-scoped isolation from the first commit.
Deployment
Into your environment, as infrastructure code you keep.
- The platform deployed in your own environment
- An agent adapted to your domain, connected to your data and tools
- The full source, owned by you
- LangGraph
- Agent orchestration and state
- pgvector
- Semantic memory and skill selection
- MCP
- Tool servers for search, extraction, and domain work
- FastAPI
- Async API over Postgres, with Django ORM
- Next.js
- App Router front end, with streaming responses
- AWS
- ECS, Secrets Manager, CloudWatch
AI agents and LLM applications
Agents and LLM-driven workflows, connected directly to your data. An agent that cannot reach your warehouse, your documents, and your internal systems is a chatbot. This is a true agent that can act on your behalf, within the guardrails that you give it.
These usually start from a prototype that already works. The demo is rarely the hard part. The time goes into guaranteeing consistent, accurate behavior on your data, at scale. We work with OpenAI and Anthropic APIs, and with open-weight models when data cannot leave your environment, which is normally a governance requirement.
Agents
Scoped tightly. The tools an agent may call, and what it does when a call fails, are devised by humans and written down before anything gets implemented or shipped.
LLM workflows
Multi-step processes with checkpoints, so a run that goes wrong halfway can be inspected and resumed.
Data integration
Wired into your warehouse, database, file systems, internal APIs, and the rest, with permissions carried through.
Evaluation
A test set built from the queries your users type, so a change can be measured instead of argued about.
- Agents and workflows running against your data, in production
- Integrations to your warehouse, database, and internal systems
- An evaluation harness you can run on every change
- Cost and latency you can see, per request
- Agents
- Tool-calling flows with explicit failure paths
- Workflows
- Multi-step runs with checkpoints and resumption
- Integration
- Your warehouse, documents, and internal APIs
- Retrieval
- Vector and hybrid search, tuned on your corpus
- Evaluation
- Repeatable scoring, so changes are measured
- OpenAI
- GPT models where hosted inference is acceptable
- Anthropic
- Claude models, including long-context work
- Open weights
- Llama, Mistral and similar, in your environment
Strategy and advisement
Advisement sessions, as-necessary, to determine where AI and data investment will pay off, and where it will not. A written assessment of our opinions on what to build, what to buy, and the reasoning behind each, in a form you can hand to someone who was not in the room.
Advisement can be a one-time session, or a series of sessions over the course of weeks, months, or years. This is the least expensive way to start with us, and the fastest way to find where the value is in your project. Regular advisement sessions have saved some of our clients countless hours of engineering and research time on problems that they otherwise would have had to figure out through trial and error. They also serve as a shortcut when planning a complex product that you want to make sure has strong foundations.
Readiness
Whether your data can support what you want to build, answered before anyone budgets for it.
Planning
Designing systems that will scale, will have a positive impact, won't blow up costs, and that have an achievable scope
Sequencing
What to do first, and what gets cheaper once something else exists.
Risk
Where the governance, vendor, and key-person exposure sits.
- A written assessment, not a slide deck
- A sequenced plan, and why each step is where it is
- An explicit list of what not to build
- Readiness
- Whether your data can support what you want
- Planning
- How do we build this?
- Roadmap
- A plan you can hand to a board
- Risk review
- Governance, vendor, and key-person exposure
Data platforms and warehouses
More than just ingestion, storage, and transformation (ETL). In practice this means orchestration, a columnar warehouse, Postgres, or NoSQL database chosen against your actual data shape and volume, and a modeling layer with tests, so that two people asking for revenue get the same number.
We also take on migrations off legacy ETL, including the ones where the original authors have left and the business logic sits in stored procedures or APIs no one has touched in years. We scope that work accurately, because we have done it before. We have dealt with databases with billions of data points, across fields ranging from restaurants to social media. Different types of data require different treatment, and we have worked with some of the most unwieldy data that the internet has to offer.
Sizing
Warehouse choice follows your read patterns and volume. Most teams are talked out of something larger than they need.
Modeling
A tested transformation layer, so definitions live in one place.
Orchestration
Scheduled and observable. A failed run should be obvious, and re-runnable without a person on call.
Access
Row- and column-level permissions defined once, in the modeling layer.
Migration
Legacy ETL moved incrementally, with old and new reconciled against each other before anything is switched off.
- A warehouse your team can query without asking us
- Transformations under version control, with tests
- Documented definitions for the metrics that matter
- A runbook for the pipelines, written for whoever is on call
- AWS
- S3, RDS, Glue, Lambda, and the rest as needed
- Postgres
- Right for longer than most teams expect
- Warehouses
- Redshift, Snowflake, BigQuery, DuckDB
- Orchestration
- Airflow, Dagster, or something simpler
- Modeling
- dbt-style transformations, under version control
- Testing
- Assertions on the data, not just the code
Analytics, BI, and machine learning
Metrics layers, dashboards, forecasting, and predictive models. The decisive work is often upstream of the dashboard: agreeing on what a metric means before anyone builds a chart of it. Unresolved definitions are the most common reason a reporting project stops being used.
Where a model earns its place we build it and evaluate it properly, so you know what it is worth. When a simpler approach performs just as well, we will say so, and build that instead.
Definitions
Written down and agreed before the first chart.
Dashboards
Built for whoever has to make the decision, whether in a customer-facing web application, PowerBI, or any other analytics platform.
Forecasting
With transparent uncertainty. The most important metric to measure in forecasting is the one most-often omitted: consistency. A forecast that is right 99% of the time is useless if it is off by 100% one percent of the time.
Models
Compared against at least one simple baseline every time. The simplest models are almost always the best. If you need something more complex, you had better know it is better than a regression.
- A metrics layer with definitions everyone has signed off on
- Dashboards that get used after the launch meeting
- Models with measured performance against a baseline
- A straight answer on where prediction helps and where it does not
- Metrics
- One agreed definition per metric, documented
- Forecasting
- Time series with stated uncertainty
- Models
- Designed to be simple and effective, and tested to prove it
- Reporting
- Dashboards and the queries behind them
Tell us what you are trying to build.
Thirty minutes. Describe the problem and we will tell you how we would approach it, whether or not you hire us for it.