We Build What Others
Say Can't Be Built.

Problem Framing & Feasibility

Before touching a model, we define the exact business problem, establish baseline metrics, and determine whether ML is the right tool. Most projects that fail do so here.

Data Architecture & Pipeline

We design and build the data infrastructure your models need: ingestion, labeling, versioning, and the feature store that ensures training and inference see the same world.

Model Development & Fine-Tuning

Foundation model selection, fine-tuning on proprietary data, RLHF where applicable, and rigorous offline evaluation against held-out benchmarks.

Inference Infrastructure

GPU cluster provisioning, model serving (Triton, vLLM, custom), batching strategy, quantization, and the observability layer to catch drift before it costs you.

Production Handoff & Support

Runbooks, monitoring dashboards, alerting thresholds, and optional embedded support during the critical first 90 days post-launch.

Architecture Review & Design

We spend real time on design before writing code. Domain modeling, API contracts, consistency guarantees, and failure mode analysis. This is where the expensive mistakes don't happen.

Iterative Delivery in Production Increments

We ship to a staging environment that mirrors production from sprint one. No "big bang" releases. Each increment is deployable, observable, and reversible.

Performance Engineering

Load testing against realistic traffic models, query optimization, caching strategy, and connection pool tuning — before you discover you need it.

Security & Compliance Integration

Threat modeling, input validation, secrets management, audit logging, and compliance attestation built in — not bolted on at the end.

Documentation & Ownership Transfer

Architectural decision records, runbooks, on-call playbooks, and knowledge transfer sessions so your team fully owns what we built.

Discovery & Current State Audit

We map your existing infrastructure: resource inventory, IAM surface, network topology, cost breakdown, and active threat exposure. This takes days, not weeks.

Target Architecture Design

Reference architecture tailored to your workloads, compliance posture, and reliability requirements. Multi-region where it matters, single-region where it doesn't. IaC from day one.

Infrastructure as Code Implementation

Terraform modules (or CDK for AWS-native teams), versioned state, and CI/CD pipelines that apply infra changes through the same review process as application code.

Reliability Engineering

Chaos engineering exercises, runbook automation, alerting that pages on symptoms not causes, and SLO/SLA instrumentation.

FinOps Optimization

Right-sizing, reserved capacity strategy, spot instance integration, cost allocation tagging, and quarterly review cadence to prevent cloud spend from growing faster than revenue.

Developer Experience Audit

We interview engineers, map the deployment lifecycle, and measure the gap between "code merged" and "code in production." This baseline informs every subsequent decision.

CI/CD Pipeline Design & Implementation

GitHub Actions, ArgoCD, or custom pipelines — always with standardized stages: lint, test, security scan, build, deploy, smoke test, rollback trigger.

Internal Developer Platform

Backstage-based or custom IDP: service catalog, golden path scaffolding, self-service environment provisioning, and documentation co-located with code.

Observability Stack

Structured logging, distributed tracing (OpenTelemetry), metrics (Prometheus/Grafana or Datadog), and alert routing that sends the right signal to the right team.

On-Call Enablement

Runbook automation, incident management tooling, post-mortem framework, and on-call rotation design. The goal: incidents stay small and happen less often.