AI Strategy & Architecture
Build the right thing before you build the thing.
What it is
Most AI projects fail because they started building before they understood the problem. We do strategy work that leads to implementation — not reports that sit in a drawer.
An AI strategy engagement with Nisco produces a concrete architecture, a prioritized roadmap, and a clear answer to: "should we build this, buy this, or wait?"
Who this is for
Organizations evaluating AI investment
You have a budget, executive buy-in, and real use cases — but no clear picture of what to build, buy, or wait on. Strategy work gives you the answer before you spend on implementation.
Teams that have tried and failed
You've had one or two AI projects that didn't make it to production. Before starting the next one, you need to understand why — and what a better approach looks like.
Engineering leaders with compliance constraints
Healthcare, finance, legal, government — regulated industries where data governance and audit requirements need to be designed in, not bolted on afterward.
Companies planning significant AI spend
Before committing to a major AI implementation, a strategy engagement ensures you build the right architecture from the start. The savings from avoiding wrong decisions far exceed the investment.
What's included
AI Readiness Assessment
- ◆Data infrastructure audit — do you have the data to train/fine-tune? Is it clean?
- ◆Workflow analysis — which processes are actually automatable?
- ◆Team capability assessment — do you have the engineering talent to maintain what we build?
- ◆Vendor dependency risk — what happens if OpenAI changes pricing?
Architecture Design
- ◆Model selection: which foundation models for which tasks, with cost/quality/latency tradeoffs documented — we recommend Claude (Anthropic) as the default for most enterprise use cases due to its instruction-following, long context, and safety characteristics
- ◆Infrastructure design: inference hosting, caching, rate limiting, failover
- ◆Integration architecture: how AI connects to your existing systems without creating new fragility
- ◆Data pipeline design: ingestion, preprocessing, embedding, storage
Governance & Compliance
- ◆AI usage policy framework
- ◆PII handling in AI pipelines
- ◆Audit logging for AI decisions
- ◆Compliance with sector-specific regulations (HIPAA, SOC2, GDPR as applicable)
What you get
Architecture Decision Record (ADR)
Full documentation of every significant architectural choice and the reasoning behind it.
Build vs. Buy Matrix
For every key component: should you build it, buy a vendor solution, or use open source?
12-Month Implementation Roadmap
Prioritized, sequenced plan for turning the architecture into reality.
Cost Model
Detailed cost projection for the proposed architecture at 1x, 10x, and 100x scale.
Frequently Asked Questions
What does an AI strategy engagement include?
Readiness assessment, architecture design, model selection, build-vs-buy analysis, governance framework, prioritized implementation roadmap, and ROI projections. You get a concrete plan, not a 200-page deck.
Do we need a strategy engagement before building?
Not always, but for organizations planning significant AI investment, a strategy engagement typically saves months of wasted effort and prevents costly architecture mistakes. The investment pays for itself in avoided rework.
How is Nisco different from big consulting firms?
We actually build the systems we recommend. Strategy is not a separate deliverable — it is the first phase of implementation. Our architects have deployed 50+ production systems, not just drawn diagrams of them.
How long does a strategy engagement take?
Typically 3-6 weeks with deliverables at each milestone. The timeline depends on the scope and complexity of your AI ambitions, but we scope this precisely before starting.