What We Cover
The AI landscape changes faster than any technology market in history. New models, new frameworks, and new vendors appear weekly. We help you cut through the noise and make technology decisions grounded in your specific business context, not hype cycles.
Vendor Selection
We evaluate AI platforms, SaaS tools, and service providers against your actual requirements. Our assessments include pricing models, data handling policies, integration complexity, and long-term viability. No vendor pays us referral fees.
Build vs Buy
The most expensive mistake in AI is building what you should buy, or buying what you should build. We analyze your use case specifics, competitive dynamics, and internal capabilities to make the right call for each opportunity.
Architecture Planning
We design system architectures that accommodate AI workloads without requiring you to replace your existing infrastructure. This includes API design, data pipeline planning, model serving strategies, and fallback patterns.
LLM Evaluation
Not every problem needs a flagship model. We benchmark language models against your actual use cases, measuring accuracy, latency, cost per request, and data privacy implications. The right model is often not the most expensive one.
Strategy Process
Requirements
Define what you actually need
Landscape
Map options against requirements
PoC Design
Test assumptions that matter
Decision
Structured comparison with recs
Requirements
Define what you actually need
Landscape
Map options against requirements
PoC Design
Test assumptions that matter
Decision
Structured comparison with recs
Technology Strategy Services
Our Process
Technology strategy work typically spans three to four weeks. We move quickly because delayed decisions in this space have real cost.
- Requirements definition. We work with your team to articulate what you actually need, not what vendors have told you to want. This includes functional requirements, non-functional requirements, compliance constraints, and budget parameters.
- Landscape analysis. We map the relevant technology options against your requirements. This is where our hands-on experience with dozens of AI tools and platforms pays off. We know what works in practice, not just in demos.
- Proof of concept design. For the top candidates, we define a focused proof of concept that tests the assumptions that matter most. We scope these to be completable in days, not months.
- Decision framework. We deliver a structured comparison with clear recommendations. Where trade-offs exist, we lay them out honestly so your leadership team can make informed choices.
Strategic Deliverables
- Technology recommendation report. A detailed comparison of evaluated options with scoring across performance, cost, integration effort, scalability, and risk. Each recommendation includes the reasoning behind it.
- Architecture blueprint. A technical design document showing how the recommended technology fits into your existing systems. Includes data flow diagrams, API specifications, and infrastructure requirements.
- Cost model. Projected costs across a 12-month horizon, including licensing, infrastructure, development effort, and ongoing maintenance. We model best-case, expected, and worst-case scenarios.
- Implementation roadmap. A phased plan for adopting the recommended technology, with clear milestones, resource requirements, and decision gates. Designed to deliver value incrementally rather than in a single large deployment.
- Risk register. A candid assessment of what could go wrong: vendor stability, technology maturity, integration challenges, and organizational adoption risks. Each risk includes a mitigation strategy.
Common Scenarios
These are the situations where technology strategy work delivers the highest return on investment.
- Choosing an AI platform. You have been pitched by three vendors and each claims to be the best fit. You need an independent evaluation before signing a multi-year contract.
- Modernizing a legacy system. Your core business system is aging and you are evaluating whether to add AI capabilities to it, replace it with an AI-native alternative, or build a custom solution.
- Scaling a successful pilot. Your proof of concept worked, but the architecture that supported ten users will not support ten thousand. You need a production-grade design before scaling.
- Reducing AI costs. Your AI spend is climbing faster than the value it delivers. You need to evaluate whether you are using the right models, the right providers, or the right approach entirely.