Onboard in Days.
Deliver From Week One.
Whether you need a developer in your team by Monday or a full AI platform built in 16 weeks — we have a clear, transparent process for both. No surprises. Measurable progress at every stage.
From Brief to Productive
in Under 5 Days
When you need a developer, tech lead, QA engineer, or BA embedded in your team — our onboarding process gets them productive without the usual weeks of interviews, notice periods, and ramp-up confusion.
Brief (Day 1)
You tell us the role, tech stack, team context, and engagement model. We ask the questions that matter — no lengthy RFP process. A 30-minute call is usually enough.
Match (Day 1–2)
We match your brief to the right person from our team. You get a profile and a short technical context document — not a CV stack. If you want a brief call with the candidate, we arrange it. We do not waste your time with mismatched profiles.
Contracts (Day 2–3)
Standard vendor services agreement — our template or yours. Covers scope, SLAs, IP, confidentiality, and billing terms. We move fast; most clients are signed within 48 hours of agreeing terms.
Productive (Day 3–5)
Your new team member attends your standup, joins your tools (Jira, Slack, GitHub), and starts contributing. We handle access setup, tooling configuration, and context download — you get productivity, not an onboarding project.
Every CompCode Solutions team member has a named engagement manager from our side. You have a direct escalation path, weekly delivery check-ins, and a monthly performance review — vendor-grade accountability, not freelancer-style ambiguity.
When the Goal is Building AI Into Your Product
For engagements where CompCode Solutions leads the full AI build — not just supplying people — we follow our structured four-phase delivery process below.
AI Readiness Assessment
Before architecture. Before code. Before anything. We need to know what we are working with. The AI Readiness Assessment scores your organisation across five dimensions — honestly, not as a formality to get to the next stage.
If the data is not ready, we say so. If the culture will block adoption, we surface it. If the infrastructure has a gap that will cause production failures, you know before you spend budget on build. We have seen too many AI projects fail not because of bad models but because of problems that could have been identified in week one.
Five Dimensions Assessed
AI Readiness Report — a scored assessment across five dimensions, with specific blockers identified, quick wins highlighted, and a 90-day remediation roadmap for each gap.
Go / No-Go Recommendation — an honest recommendation on whether to proceed to architecture, defer pending remediation, or pivot the scope to a more achievable starting point.
Duration: 1 week · Format: Fixed-price · Outcome: Decision document + roadmap
Architecture Specification Document — context map, agent capability cards, cloud architecture blueprint, technology stack with rationale, and 3 ADRs for the major decisions.
Migration Path — detailed sprint plan for Phases 2-3, with specific milestones, success criteria, and reversibility checkpoints at each phase boundary.
Duration: 2-3 weeks · Format: Fixed-price · Outcome: Blueprint you can take anywhere
Architecture Blueprint
Current-state to future-state mapping. We design the architecture before writing code — not because we are old-fashioned, but because changing an agentic platform architecture in production is significantly more expensive than changing it on paper.
Every agent receives a Capability Card — a formal specification defining its goal, inputs, outputs, tools, blast radius, confidence thresholds, and HI Authorization conditions. This is the contract that all subsequent work is held to. Architecture Decision Records (ADRs) capture the reasoning behind major choices, so future teams understand why — not just what.
Phased Build:
Shadow → Autonomous
We do not deliver a finished system at week 12 and call it done. We deliver working software at each sub-phase — with measurable quality at each stage — so you see value before full autonomy is granted.
Shadow Phase
Agents run against real production data. Outputs are produced but not acted on. Human and agent outputs are compared side-by-side. Divergence rate tracked over time. This phase typically runs 2 weeks minimum to build a statistically meaningful comparison dataset.
Exit criterion: Divergence rate below agreed threshold for 5 consecutive business days.
Assisted Phase
Agent recommendations are used by humans to make faster, better-informed decisions. Humans still decide — but they are working with the agent's analysis. Escalation rate tracked. Quality of human decisions improves measurably because agents surface information humans would not have found quickly.
Exit criterion: Human decision quality improves; agent recommendation acceptance rate above agreed threshold.
Supervised Phase
Agent acts autonomously for low-risk, high-confidence decisions. Humans review exceptions only — surfaced by the HI Authorization gate. Exception review includes full evidence chain. This is when the operational efficiency gains become visible and measurable.
Exit criterion: Exception rate stable; audit review confirms all escalations are appropriate.
Autonomous Phase
Agent handles all decisions within defined scope with quality monitoring active. Humans focus on exceptions and quality oversight — not routine processing. Goal completion rate, escalation rate, cost per workflow, and hallucination rate all monitored on a live dashboard.
Ongoing: Monthly quality reviews, eval pipeline regression tests, governance audits.
Production Hardening
This is where we turn a working agentic system into a production-grade one. It is the phase most consultancies skip — and why most AI deployments degrade within six months.
Idempotency Audit
Every agent operation that can be retried is verified to be safe to retry. Exactly-once delivery where required, at-least-once where tolerated. No silent double-processing of financial transactions or duplicate emails.
Crash Recovery Testing
Temporal checkpoint replay verified under simulated worker failure scenarios. What happens when the agent crashes at step 23 of 47? We test it before production finds out.
Observability Dashboards
Langfuse + OpenTelemetry dashboards for goal completion rate, escalation rate, tool failure rate, cost per workflow, and token budget burn. Alerts configured for anomaly detection.
Governance Policy
Written AI governance policy: accountability mapping, HI Authorization thresholds, audit review cadence, model version governance, and escalation procedures. Compliance-ready documentation.
HI Authorization UX
The human review interface for exception approval: evidence chain display, time-boxing UI, decision capture with reasoning field, and audit log integration. Humans should find reviews clear and fast — not burdensome.
Stakeholder Pack
Architecture overview for non-technical stakeholders, KPI dashboard for leadership, and a clear explanation of what the system does — and what it cannot do. Managing expectations is part of production hardening.
Team Enablement & CoE
We do not want clients to depend on us forever. The most successful engagements end with your internal team owning the system — understanding every architectural decision, running the eval pipeline themselves, and extending the platform confidently.
Phase 4 covers technical training on the platform patterns, Centre of Excellence (CoE) structure and role definitions, standards documentation to prevent AI debt accumulation, and optional ongoing eval monitoring retainer.
The goal of every CompCode Solutions engagement is to be unnecessary within 12 months. We design for handover from day one — well-documented ADRs, clear capability cards, observable systems, and teams that understand why, not just how.
Common Questions
Depending on scope and infrastructure readiness, engagements typically take 8–16 weeks from first Discovery Call to a production-hardened system. The Readiness Assessment in week 1 provides a more precise estimate based on your specific blockers and constraints.
No. The AI Readiness Assessment is specifically designed to measure your current state honestly. We have worked with organisations ranging from zero AI maturity to those with established data science teams. The Architecture Blueprint will be calibrated to your starting point.
HI Authorization is our implementation of Human-in-the-Loop patterns as a first-class architectural primitive — not a UI button. It defines exactly when an autonomous agent must pause and seek human approval, based on confidence thresholds, blast-radius analysis, and action type classification. In regulated industries, it is the mechanism that makes AI deployable at all.
Three key differences: We use AI-accelerated development ourselves — which means faster delivery at the same quality. Eval pipelines run from sprint 1, not after delivery — so quality is measurable throughout, not assumed. And we deliver in phases with working software at each stage, not a big bang at the end of a long engagement.
Both, depending on scope certainty. Phase 0 (Readiness Assessment) and Phase 1 (Architecture Blueprint) are fixed-price. Phases 2 and 3 can be structured as time-and-materials sprints or fixed-price milestones once architecture is defined. We will recommend the right model for your situation during the Discovery Call.
Ready to Start with Phase 0?
Schedule a free Discovery Call. We will assess your situation and tell you honestly what we think the right starting point is — even if that is not an engagement with us right now.