GenAI readiness assessment
We review data quality and access, cloud platforms, security posture and team skills to show what is ready now and what needs work before generative AI can scale.
Generative AI
Generative AI consulting that cuts through hype.
Our generative AI consulting helps leadership teams decide where GenAI genuinely pays off, where it adds risk, and what to build first. You get an honest, technically grounded plan instead of a vendor pitch or a stack of trend reports.
- Vendor-neutral advice
- Use-case prioritization
- Governance from day one
01 Overview
A clear generative AI strategy your teams can execute
Boards want an AI plan, employees are already pasting data into public chatbots, and every software vendor claims a copilot. Generative AI consulting brings order to that noise. We interview the people doing the work, map the processes where language and documents dominate, and separate the opportunities worth funding from the ones that only look good in a demo.
Our generative AI strategy work is led by engineers who have shipped these systems, so recommendations come with realistic effort, data and risk assessments attached. We cover build-versus-buy decisions, model and platform selection, security and governance policy, and the skills your teams will need. The goal is a sequenced roadmap where each step proves value and lays groundwork for the next.
02 Capabilities
Generative AI Consulting capabilities.
End-to-end Generative AI Consulting: strategy, engineering and the production operations that keep it working.
Use-case discovery workshops
Structured sessions with business and technical stakeholders surface candidate ideas, then AI use case prioritization scores each one on impact, feasibility, risk and data dependency.
Build, buy or partner analysis
We compare off-the-shelf tools, platform features and custom builds for each priority use case, weighing lock-in, total cost of ownership and how much differentiation is at stake.
Model and platform selection
Side-by-side evaluations of hosted and open-weight models on your own sample tasks, plus guidance on cloud AI platforms, vector databases and orchestration frameworks.
Generative AI governance
Acceptable-use policies, data classification rules, human-review requirements and model risk processes, designed around GDPR, HIPAA or SOC 2 requirements where they apply.
Pilot design and change management
We define a pilot with measurable success criteria, a target user group and a feedback loop, then plan training and communication so adoption does not stall after launch.
03 How we deliver
From first call to production.
Every Generative AI Consulting engagement follows the same five stages, so you see value early and risk stays low.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
04 Use cases
Where it pays off.
Practical, high-value applications we design and build for clients across industries.
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UC-01
Executive AI strategy for the board
Translate GenAI possibilities into a concise investment case leadership can defend, covering opportunity areas, risks, funding phases and the decisions needed this quarter.
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UC-02
Rescuing a stalled GenAI pilot
Diagnose why a proof of concept never reached production, whether the cause is data, scope, accuracy or ownership, and lay out a concrete path to restart or retire it.
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UC-03
Shadow AI and policy rollout
Assess how employees already use public AI tools, then introduce approved alternatives and clear rules that reduce data leakage without blocking productive experimentation.
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UC-04
Vendor and platform due diligence
Evaluate AI features pitched by software vendors against your requirements, testing claims on representative data before you sign multi-year platform commitments.
05 Technology
The stack we use.
Proven, well-supported tools chosen per project for accuracy, cost and long-term maintainability.
Model ecosystems
- OpenAI
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
Cloud AI platforms
- Azure AI Foundry
- AWS Bedrock
- Google Vertex AI
- Databricks
- Snowflake Cortex
Evaluation and governance
- LangSmith
- Ragas
- Promptfoo
- NIST AI RMF
- ISO/IEC 42001
06 Industries
Built for your sector.
We deliver Generative AI Consulting for regulated and fast-moving industries alike.
What does a generative AI consulting engagement involve?
Typically we start with stakeholder interviews and a readiness assessment, run use-case discovery workshops, then score and prioritize opportunities. From there we produce a roadmap, governance recommendations and a pilot plan. Some clients stop at strategy; others continue into implementation with the same team, which keeps continuity between the plan and the build.
How do we choose which generative AI use cases to start with?
We score each idea on business value, technical feasibility, data availability, risk and time to measurable results. Good first projects are usually high-volume, text-heavy tasks with a clear quality bar and a human reviewer already in the loop. Those build confidence and infrastructure that later, more ambitious use cases can reuse.
Are your recommendations tied to a specific AI vendor?
No. Our generative AI consulting services are vendor-neutral. We benchmark multiple models and platforms on your actual tasks and recommend what fits your requirements, existing contracts and risk tolerance. Where it makes sense, we design for portability so you can change providers as pricing and capabilities shift.
What does generative AI governance cover?
It covers who may use which tools, what data can be sent to models, when human review is mandatory, how outputs are logged and audited, and how new use cases get approved. We tailor the framework to your regulatory context and align it with existing security and privacy policies so it does not become a parallel bureaucracy.
Do we need clean, centralized data before starting with GenAI?
Not necessarily. Many valuable generative AI use cases work with documents and knowledge that already exist, even if scattered. A readiness assessment identifies which priority use cases depend on data work and which can start now. We would rather sequence data improvements alongside early wins than delay everything for a large data program.