AI readiness assessment
A structured review of data quality, infrastructure, skills and processes that shows how prepared your organization is and what to fix before scaling AI work.
AI Services
AI consulting services grounded in engineering reality.
Our AI consulting services help leadership teams decide where AI is worth the investment and where it is not. Advice comes from practitioners who build and run these systems, so every recommendation reflects real constraints around data, cost, risk and delivery.
- Vendor-neutral advice
- Practitioner-led workshops
- Actionable roadmaps
01 Overview
Clear answers before you commit budget to AI
Plenty of AI initiatives stall because they started with a technology instead of a problem. Our AI consulting services reverse that order. We interview stakeholders, map workflows, review the data you actually have and estimate the effort behind each idea, then separate the quick wins from the long bets and the projects that simply should not happen yet.
The output of AI strategy consulting should be something your teams can act on Monday morning. You get a ranked portfolio of use cases, a reference architecture, build-versus-buy guidance, a governance outline and a phased AI roadmap with clear decision points. If you want help executing, the same people can stay on through delivery.
02 Capabilities
AI Consulting capabilities.
End-to-end AI Consulting: strategy, engineering and the production operations that keep it working.
Use-case identification workshops
Facilitated sessions with business and technical leads to surface candidate ideas, estimate impact and effort, and agree on which ones deserve a prototype.
AI strategy and roadmap
A phased plan that sequences initiatives, defines success metrics and links each investment to a business objective leadership already cares about.
Architecture and vendor selection
Neutral comparison of model providers, cloud platforms and tooling, with a reference architecture that fits your security posture and avoids unnecessary lock-in.
Governance and risk frameworks
Policies for acceptable use, data handling, human oversight and model review, designed around GDPR, HIPAA or SOC 2 requirements as your sector demands.
Proof-of-concept validation
Short, time-boxed experiments on your real data that test the riskiest assumption in a use case before you commit to a full build.
03 How we deliver
From first call to production.
Every 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
Setting a company-wide AI strategy
Executive teams get a shared view of opportunities, risks and investment priorities, replacing scattered experiments with a coordinated AI implementation strategy.
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UC-02
Rescuing a stalled AI pilot
We diagnose why a pilot is not reaching production, whether data, scope, evaluation or ownership, and recommend a path to finish, pivot or stop.
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UC-03
Choosing between AI platforms and vendors
Side-by-side evaluation of models and tools against your requirements, tested on representative tasks rather than relying on vendor benchmarks alone.
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UC-04
Preparing data for future AI work
A practical plan for consolidating, cleaning and documenting data assets so later machine learning and generative AI projects start from solid ground.
05 Technology
The stack we use.
Proven, well-supported tools chosen per project for accuracy, cost and long-term maintainability.
Model platforms evaluated
- OpenAI
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
Cloud and data
- AWS
- Azure
- Google Cloud
- Snowflake
- Databricks
Delivery tooling
- LangChain
- MLflow
- Weights & Biases
- Kubernetes
06 Industries
Built for your sector.
We deliver AI Consulting for regulated and fast-moving industries alike.
What do AI consulting services typically deliver?
Deliverables usually include a prioritized list of use cases, a readiness assessment covering data and infrastructure, a reference architecture, build-versus-buy guidance, a governance outline and a phased roadmap. The exact mix depends on your goals; some clients want a broad strategy, while others need a focused answer about one high-stakes initiative.
How is an AI readiness assessment conducted?
We combine stakeholder interviews, a review of data sources and pipelines, an inventory of current tools and a look at team skills and processes. Each area is rated against what your target use cases require, and the gaps are translated into concrete remediation steps with rough effort levels.
Do you recommend specific AI vendors?
We stay vendor-neutral and have no reseller arrangements that influence recommendations. When choosing models or platforms, we define criteria with you, such as accuracy, latency, cost, data residency and support, then test shortlisted options on representative tasks so the decision rests on evidence.
Can AI strategy consulting help a small company or startup?
Yes. Smaller teams often benefit most because they cannot afford to spend months on the wrong idea. For startups, engagements are usually lighter and faster, focusing on which AI capability will differentiate the product, what to build in-house and what to rent through APIs.
What happens after the consulting engagement ends?
You keep all the deliverables and can execute with your own team, another partner or us. Many clients ask us to run the first prototype so the roadmap is validated quickly. Either way, we structure the handover so decisions, assumptions and open questions are documented clearly.