AI that earns its place in your business.
Choose the engagement that fits where you are.
Most engagements start in one of these and grow into the next.
AI strategy and readiness
Decide what to fund this quarter, what to defer and what to skip. We give leadership a clear read on your data, people and budget before money moves.
- AI readiness assessment across strategy, data, talent and governance
- Roadmap sequenced to your budget cycles
- Business cases with success measures your CFO will accept
What’s involved
A candid read on your environment, your data, your people and the realities of getting anything funded inside the organization. AI governance and success criteria tied to specific numbers. The what to hold off on half of the conversation is usually the more useful one.
AI adoption and enablement
Licences are bought, but usage stalls team by team. The tool is rarely the problem; the workflow is. We redesign how work gets done, then measure the return.
- AI tool setup, permissions and governance
- Workflow redesign, training and change management
- Adoption tracked against numbers your CFO recognizes
What’s involved
Microsoft 365 Copilot and other AI tool configuration, including the permissions and governance pieces most rollouts skip. Hands-on training and follow up for the people who use the tools daily. Baselines set before rollout so value can be measured. We recently designed an AI adoption pilot for an infrastructure technology company’s engineering team: tool selection, before and after baselines and a rollout path, measured before anything scales.
AI-powered engineering
Vendor benchmarks run on clean code. We pilot AI coding tools head to head on your codebase, so you invest based on your own results.
- AI coding tool pilots across Copilot, Cursor, Claude Code and others
- Productivity baselines that hold up in a budget review
- CI/CD and pipeline integration built to last
What’s involved
Structured pilots in your environment, against your codebase and engineering standards, comparing GitHub Copilot, Cursor, Claude Code, CodeRabbit and others. AI-assisted software development on work in flight during the pilot. A recommendation you can take to a CFO with a real number attached.
Agentic AI and platform development
We design and build AI agents that act across your CRM, documents, ticketing and data, with the security and infrastructure to run them in production.
- AI agent platform architecture, permissions and security
- Model Context Protocol (MCP) servers, connectors and integrations
- Agentic workflows and multi-agent orchestration
What’s involved
Connector and integration work across CRM, ERP, internal databases and SaaS tools. Retrieval-augmented generation (RAG) pipelines tuned to your data. LLM orchestration and tool use frameworks. Multi-agent systems when the use case justifies it, single agents when it doesn’t. Cloud AI infrastructure, API integration and the governance layer security will ask about.
What we’ve learned building enterprise AI.
The tool is rarely the problem.
Adoption stalls when workflows aren’t redesigned and nobody measured the baseline.
We’re not on any vendor’s referral list.
So we recommend what fits your stack and your budget, even when it’s the cheaper option.
The people who advise you build it.
Strategy and delivery are run by the same team, from first meeting to production.
Most of the value shows up after launch.
We stay engaged while adoption takes hold, tuning systems and the operating model as the technology changes.
Secure AI agent infrastructure for a research firm.
We designed and built the environment its analysts run AI agents in: a model flexible framework, connectors to internal documents, paid research and public filings, per project client isolation and full cost tracking, then handed it to the firm’s IT team to run.
See more client work →- Agent framework that works with any AI model
- Connectors to internal and paid data sources
- Per project client isolation and cost tracking
- Runbooks and a clean handover to IT
What we build with.
What’s in active rotation across current engagements. We choose whatever fits the work and your stack.
An AI readiness assessment your leadership team can run on its own.
Twenty questions across four dimensions (strategy, data, talent and governance), with a 1-to-5 scale and an interpretation guide written so a leadership team can score it themselves on a Tuesday afternoon and have a useful internal conversation about the results without us in the room.
We hand it out because the firms that benefit from talking to us tend to be the ones scoring below 2.5 on at least one of the four dimensions. If you score above that across the board, you may not need a consulting partner at all, which is the kind of thing we'd rather tell you in advance.
Common questions.
What AI consulting services does BroadPoint offer?
BroadPoint helps organizations at every stage of AI adoption: strategy and readiness, adoption and enablement of tools like Microsoft Copilot, AI-powered software engineering, and building AI agents and platforms connected to business systems.
How do we know if our organization is ready for AI?
Start with a readiness assessment across strategy, data, talent and governance. BroadPoint's AI readiness checklist has twenty questions a leadership team can score on its own; a score below 2.5 in any area usually shows where to focus first.
Does BroadPoint recommend specific AI vendors?
BroadPoint has no commercial reason to prefer one vendor over another, whether Azure OpenAI, Anthropic, Copilot or Cursor. Recommendations are based on your environment, your data and your budget.
Can BroadPoint build AI agents that connect to our internal systems?
Yes. BroadPoint builds AI agents and platforms that connect large language models to CRM, ERP, document repositories, ticketing and internal databases, including Model Context Protocol (MCP) servers, retrieval augmented generation pipelines and the security and permissions around them.
