AI Strategy & Implementation — From PoC to Production

The Problem We Solve

Most enterprise AI initiatives fail in production, not in the PoC. The PoC works on cherry-picked data with a senior engineer babysitting it. Then it goes to production and meets real input distributions, real PII constraints, real cost ceilings, and real users who do unexpected things — and the demo collapses.

We're hired by C-suite leaders who've seen one or more of these failures and want the next AI investment to actually ship, measurably perform, and survive an audit.

Our Approach

We run AI engagements in three phases, each with hard exit criteria:

Phase 1 — Discovery & Architecture (4 weeks, fixed scope)

• Audit existing data assets, integration points, and team AI literacy.
• Identify the 1–3 highest-ROI workflows ranked by (value created × probability of shipping) ÷ implementation cost.
• Produce: architecture diagram, eval-harness specification, governance plan, cost model with per-1000-task projections.

Phase 2 — Build (8–10 weeks, milestone-billed)

• Implement the chosen workflow end-to-end, including: retrieval layer, model orchestration, output evaluation, observability, and a documented rollback path.
• Ship to production behind a feature flag with shadow-mode evaluation against the existing process.
• Promote to live traffic only after eval metrics meet the targets defined in Phase 1.

Phase 3 — Operationalization (4 weeks)

• Hand over runbooks, on-call procedures, and the eval harness to your team.
• Train internal owners on the cost-per-task dashboard and the policy enforcement layer.
• Define the quarterly review cadence: cost trajectory, accuracy trajectory, business outcome.

What's Included

• Production-grade RAG, agentic workflows, or ML pipelines (depending on use case)
• Evaluation harness with golden-set regression testing
• Cost instrumentation with per-task and per-tenant breakdown
• Policy enforcement layer (PII redaction, content filtering, prompt-injection defense)
• Audit logs structured for SOC 2, ISO 27001, and EU AI Act compliance
• Internal runbook and quarterly review template

Who This Is For

CTOs and CIOs at SMBs and mid-market companies who:

• Have one or more failed AI PoCs they need to replace with shipped systems
• Need AI to integrate with existing data and identity infrastructure (not a side-channel skunkworks)
• Have measurable business outcomes they expect AI to move (case resolution time, content production rate, cost-per-decision)
• Need governance that survives a real audit, not just a slide deck

Expected Outcomes

After a typical 16–18 week engagement:

• One AI workflow live in production with 30+ days of clean evaluation data
• Cost-per-task at or below model-economy projections from Phase 1
• Business KPI movement within the range projected in Phase 1 (we publish ranges, not point estimates, because production AI is probabilistic)
• Internal team owns the system end-to-end, including the eval harness — we do not create lock-in

Why Pixel of Software

Three things distinguish our AI work from typical consultancy or boutique AI shops:

1. We're software engineers first, AI specialists second. AI in production is 80% systems engineering and 20% model selection. Most AI consultancies have those proportions inverted.
2. We measure cost-per-task from day one. This is the conversation that kills 90% of poorly-designed AI workflows in your CFO's office. We surface it early so you ship the ones that survive.
3. We bring engineering performance discipline to AI work. Our DORA-grade delivery practices apply to AI builds too — short feedback loops, measurable quality gates, no vibes-based promotion to production.