AI Skills Assessment

De-risk your AI strategy with a verified skills assessment.

A two-week, technical deep-dive into your team's capabilities, codebase readiness, and architectural maturity — delivered as an actionable roadmap.

Flat rate: $10,000 • Typical turnaround: 2 weeks • Coverage: up to 5 engineers

Timeline
2 weeks
Investment
$10k
Coverage
Up to 5
Output
Scorecard + roadmap

We can work under NDA and follow least-privilege access. If repo access is constrained, we can review via screenshare or sanitized code drops.

The AI skills gap is real

Many organizations are shipping AI features without verifying if the technical foundation — and the people building it — are ready for production.

Hiring signals are noisy

Resumes and take-homes miss generative AI nuance. We evaluate how engineers reason about RAG, evals, and safe agent design.

Architecture is often not RAG-ready

Legacy data flows, permissions, and latency constraints break production AI. We surface the bottlenecks before they become incidents.

Security risks compound quickly

Prompt injection, data leakage, and tool misuse are common failure modes when teams skip secure patterns and guardrails.

What we assess

We benchmark the skills and engineering foundations required to ship reliable generative AI systems — and we call out pragmatic next steps.

LLM application engineering

RAG patterns, tool calling, agent orchestration, and prompt/versioning conventions.

Evaluation and observability

Evals strategy, regression testing, telemetry, incident patterns, and quality gates.

Data and integration readiness

Embedding and indexing strategy, permissions, retrieval quality, latency, and cost controls.

Security and reliability

Prompt injection defenses, PII handling, secrets, auditability, and safe tool access.

Team capability and workflow

How the team designs, reviews, ships, and maintains AI features in production.

Strategy and enablement

Hiring recommendations, upskilling plan, and a pragmatic roadmap aligned to outcomes.

Deliverables you can act on

You get a defensible scorecard and a roadmap — not vague advice. Each output is designed to translate into execution.

Readiness scorecard

A benchmarked view of team capability, architecture maturity, and risk by area.

Skill matrix + gap analysis

A clear map of strengths, gaps, and role coverage across engineering and data.

Risk register

Top technical and security risks, with mitigation steps and prioritization.

90-day roadmap

A prioritized plan for foundations, quick wins, and production hardening.

Want this assessment tailored for hiring? We can also design role-specific interview loops and a structured rubric based on your stack.

Deliverable snapshot

Example output structure

Sample
Team readiness score:62/100
Strong Python fundamentals
Data engineering pipelines mature
Missing evaluation framework (evals)
Prompting and conventions inconsistent
Recommendation: Implement an eval framework before scaling to production. Upskill two senior engineers on agent orchestration (LangGraph/LangChain or equivalent).

How it works

A tight engagement designed to minimize disruption and maximize signal — with clear milestones and an executive readout.

Kickoff + context

Align on goals, success criteria, and constraints. Agree on the access model and assessment scope.

Interviews + live evaluations

45–60 minute technical interviews and pair sessions to see real problem-solving and tool use.

Codebase + architecture review

Review key services, data flows, and AI touchpoints to validate readiness for RAG and agents.

Synthesis + executive readout

Deliver the scorecard, roadmap, and recommendations — then align on the next phase.

Simple, transparent pricing

Flat rate engagement. Clear scope. Zero surprises.

Team assessment package

Designed for engineering teams shipping production AI features.

One-time fee
$10,000
Typical turnaround: 2 weeks
  • Assess up to 5 engineers
  • Two-week typical turnaround
  • Technical interviews + pair sessions
  • Codebase and architecture review
  • Risk register + 90-day roadmap
  • Executive presentation + Q&A

Frequently asked questions

Details on scope, access, and what you receive.

How long does the assessment take?

Typically 2 weeks. Week 1 is for discovery, interviews, and code review. Week 2 is for synthesis, scoring, and the executive readout.

What do you need from us?

Relevant repositories (or a curated subset), architecture notes, and interview slots with key engineers. We’ll share an intake checklist during kickoff.

Can you run this without direct repo access?

Yes. If access is constrained, we can review via screenshare, sanitized exports, or a representative slice of services — the goal is still a defensible scorecard.

Is this only for engineers?

The assessment focuses on engineering and data roles, but we also check product alignment so the roadmap reflects real constraints and outcomes.

Do you assess larger teams?

Yes. The package covers up to 5 engineers; we offer custom enterprise pricing for larger orgs or multi-team rollouts.

What happens after the assessment?

You can execute the roadmap internally, or we can support implementation, hiring loops, and team enablement — with clear milestones and handoffs.