WHAT WE DO

Secure AI, engineered right.

We work across data, AI, platform, and applications. Every engagement is scoped to your technical needs and focused on positive business outcomes.

Pillar / 01

Data Engineering

AI is only as reliable as the data underneath it. We build the real-time pipelines and event-driven infrastructure production systems depend on, and we were doing this work long before it was framed as an AI problem.

Streaming architecture

Event-driven system design: delivery guarantees, ordering, partitioning strategy, and what happens when a consumer falls behind. The decisions that determine whether a platform holds up under load or becomes an incident source.

Integration and change capture

Getting data out of source systems continuously, without the brittle batch jobs that break quietly and get discovered downstream.

Contracts and schema evolution

The versioning discipline that stops a producer change from breaking three teams six months later, and the compatibility rules that let systems evolve independently.

Stateful processing

Joins, windowing, and the state recovery questions most teams meet the hard way: how long a rebuild takes, what a rebalance does to latency, and who owns the answer.

Data observability

Monitoring the data itself. Freshness, schema drift, and end-to-end latency from event creation to final consumer, because the expensive failures are weeks of bad data flowing through healthy infrastructure.

Platform migration

Moving between streaming platforms with offset cutover, schema migration, and a rollback path that exists before anyone needs it.

Encryption in the topology

Access control, data classification, and encryption in transit are part of the topology before the first line of code.

When teams bring us in

An AI initiative is blocked by the data layer.
A streaming platform has become operational debt.
A migration is coming and nobody wants to be the one who loses messages.

Read our technical writing on real-time data engineering

Pillar / 02

AI Engineering

Production grade systems designed to run reliably inside enterprise environments and to be maintained by the team that owns them.

The application layer

Models are probabilistic. Applications can't be. We build the validation, structured outputs, retries, fallbacks, and guardrails that turn model behavior into something the rest of your system can depend on.

Agentic systems

Agent architecture, orchestration, and durable execution, so a run that fails partway can pick up where it stopped, without redoing finished work or repeating a side effect.

Memory and retrieval

What a system remembers, what it retrieves, and what the requesting user is permitted to see. Separate layers with different retention.

Evaluation

Every failure, edge case, and outcome feeds the evaluation set. Changes to prompts, models, tools, and workflows are tested against it before they ship. Build → Measure → Learn → Improve → Evaluate again.

Cost and performance

Not every task needs the largest model. We match smaller, task-specific models to routine work, route harder problems to more capable ones, and tune caching and tokens for speed. Quality, speed, and cost move together, so we optimize for all three.

Integration

Connecting agents to existing systems, with the requesting user's identity carried intact to every call.

Identity carried through

Tool permissions, access controls, and oversight are architectural decisions, made before any agent runs.

When teams bring us in

A pilot needs to become a system.
Agents need to touch production and nobody is comfortable yet.
An AI feature works in a demo and falls over in production.

Read our technical writing on AI engineering Read our technical writing on agentic AI

Pillar / 03

Platform

Where production systems run. Cloud infrastructure, deployment automation, observability, and runtime architecture that keep AI, data, and application workloads reliable under load.

Workload architecture

Scheduling, resource requests and limits, autoscaling, workload isolation, capacity planning, and infrastructure efficiency, designed so no workload starves the rest of the environment.

Runtime and scaling for AI workloads

Scale infrastructure around the way AI workloads behave. CPU and GPU capacity, horizontal and event-driven autoscaling, queue depth, concurrency limits, and latency targets are designed and measured together.

Delivery and rollback

Production changes need a safe path forward and a fast path back. Automated CI/CD, blue/green deployments, controlled rollouts, health checks, versioning, and rollback make application, model, and configuration changes repeatable and recoverable.

Policy and guardrails

Infrastructure and security policy enforced before workloads reach production. Resource limits, workload identity, network policy, secrets management, controlled outbound access, and deployment policy are part of the platform, not conventions teams have to remember.

Observability for AI workloads

When something breaks in production, teams need to see why. We use OpenTelemetry to correlate traces, metrics, and logs, so one request can be followed across applications, models, agents, and infrastructure. We instrument latency, throughput, errors, saturation, queue depth, model performance, token usage, and cost.

Cloud and network architecture

Identity, connectivity, ingress and egress, private services, and data residency are designed into the platform, not added after workloads are deployed.

Policy before deploy

Policy enforced before anything runs. Nothing deploys until it satisfies rules that were written down in advance.

When teams bring us in

AI workloads are moving onto infrastructure the team owns.
Something works but doesn't scale.
A platform needs guardrails before more teams are allowed onto it.

Read our technical writing on platform engineering

Pillar / 04

Applications

Full-stack applications with AI built in, engineered around users and production workloads.

AI-embedded product surfaces

Interfaces with AI at the product core: responses that survive a dropped connection, incremental rendering that never shows a half-finished result, and cancellation that propagates.

APIs and contracts

Designing an interface when the thing behind it is probabilistic, with validation at the boundary so unpredictability stops at one layer.

Backend engineering

Production applications have to handle more than the happy path. State management, asynchronous work, concurrency, retries, idempotency, and failure recovery are designed in, so the application behaves predictably under load.

Human-in-the-loop workflows

Approval queues, audit trails, and review surfaces that let a team put an agent into a production process before trusting it to act alone.

Operator tooling

The unglamorous internal applications that make an AI system operable: run inspection, decision review, evaluation dashboards, and manual override.

Front-end engineering

The user experience is part of the architecture from the start, not a UI layer added after the system is built. Accessibility, perceived performance, responsive interaction, loading and failure states, and the details that make an AI product feel deliberate are designed alongside the services behind them.

Authorization per request

Authorization enforced per request at the resource, with the user's identity carried all the way down.

When teams bring us in

An AI capability needs a product around it.
A prototype needs to become a production application.
An internal tool needs to be good enough that people use it.

Read our technical writing on building AI applications

WHERE TO START

Most engagements start in one of three places.

Each of these is scoped to prove something specific. They stand on their own, and they are often how a longer engagement begins.

AI Pilot

Prove a use case in production. One system, your environment, your data. Find out fast if the idea holds up.

Agent Build

Put agents to work on workflows, with approval gates and oversight. Autonomy is earned through evidence.

Data Foundation

Build the real-time data layer your AI depends on. Pipelines, streaming, and the data contracts that keep it reliable.

AI EDGE

A working AI practice.
Proof early, ownership at handoff.

Explore AI Edge

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