HOW WE WORK
Disciplined engineering from feasibility to production.
We do not sell a strategy deck. We work in phases: understand the job, prove it on your data, put it in your cloud, then hand it over.
/ 01ENGINEERING LIFECYCLE
Engineering Lifecycle
- PHASE/01Feasibility & Architecture SprintWe inspect your data sources, evaluate system boundaries, identify regulatory constraints, and formulate a concrete technical architecture specification and evaluation benchmark plan.
- PHASE/02Production Prototype & Offline EvalsWe build a functional prototype and construct an automated evaluation harness using synthetic and historical data to quantify accuracy, latency, and edge-case behavior.
- PHASE/03Production Integration & HardeningWe integrate the system directly into your cloud infrastructure, establish secure database connectors, implement rate limits and fallback mechanisms, and deploy observability dashboards.
- PHASE/04Observability, Tuning & HandoverContinuous tracking of model drift, cost-per-call, and latency in production. We train your internal engineering team on the codebase or provide ongoing operational maintenance.
- PHASE/01Feasibility & Architecture SprintWe inspect your data sources, evaluate system boundaries, identify regulatory constraints, and formulate a concrete technical architecture specification and evaluation benchmark plan.
- PHASE/02Production Prototype & Offline EvalsWe build a functional prototype and construct an automated evaluation harness using synthetic and historical data to quantify accuracy, latency, and edge-case behavior.
- PHASE/03Production Integration & HardeningWe integrate the system directly into your cloud infrastructure, establish secure database connectors, implement rate limits and fallback mechanisms, and deploy observability dashboards.
- PHASE/04Observability, Tuning & HandoverContinuous tracking of model drift, cost-per-call, and latency in production. We train your internal engineering team on the codebase or provide ongoing operational maintenance.
/ 02HOW TO EVALUATE AN AI CONSULTING PARTNER
How to Evaluate an AI Consulting Partner
Demand answers to these four questions from any firm proposing AI systems:
01
Evaluation Methodology
Do they have an automated quantitative evaluation harness, or do they rely on subjective manual spot-checking?
02
Infrastructure & Code Ownership
Do they deploy within your private VPC with full intellectual property assignment, or lock you into proprietary platforms?
03
Production Integration Capability
Can their engineers write high-concurrency backend services, or do their capabilities stop at prompt engineering?
04
Operational Drift & Governance
How do they monitor model performance degradation, latency shifts, and cost anomalies once live?
Partner with an engineering team that builds for production.