AI implementation around your business
More capacity to serve your customers. A new service you can finally deliver. A way to put your proprietary knowledge to work.
We help you choose and implement the right approach, working through your information, existing systems, team and cost of operation.
Discuss your AI project →Bring the goal and what’s getting in the way. You don’t need a technical specification.
Explore the implementation work
Connect incoming data, databases and business systems so AI can work with reliable information. Scoped data integration and infrastructure from beAIfirst.
Choose and implement private AI around your data, hosting and support requirements. Compare commercial platforms, private cloud and on-premises options.
Understand and improve AI operating costs without losing sight of quality. Review usage, models, infrastructure and cost per successful task with beAIfirst.
Before you commit
Bring the plan, workflow or requirement. These practical guides help you identify what still needs to be proved.
01 / Get it working
You may have a clear idea, an early prototype or tools you’ve already bought. We work through what the project needs, implement the agreed approach and test it with the people who will use it.
Discuss what you want to implement →The agreed implementation, a record of its testing and clear operating responsibilities.
The task works under the conditions that matter: quality, turnaround, capacity or a new service your team can deliver.
02 / Use your information
Decide what AI needs to access, where that information can be processed and who should be able to use it. We assess commercial platforms, private hosting and on-premises options, then implement the agreed approach.
Discuss your data requirements →Implemented components within the agreed data and deployment scope, with documentation of how information moves through the system.
The intended work uses authorized information within the agreed access and deployment arrangements.
Where AI runs is a business decision
These are starting points for comparison. A project can combine them. Proprietary data does not automatically require a self-hosted model.
Provider-managed environment
Model + selected data
The provider operates the service. Your team manages its use.
Your chosen cloud environment
Model + selected data
Agree who deploys, monitors and updates each component.
Infrastructure at your organization
Model + selected data
Plan for hardware, security, updates and recovery.
03 / Make the economics work
A promising demonstration does not tell you what it will cost when the whole team uses it. We assess costs before a rollout or improve a system already in use, testing the options against the quality and responsiveness your work requires.
Discuss your AI costs →A cost baseline or forecast, evaluated options and—where included—the agreed improvements and monitoring.
Total cost per successfully completed task, alongside quality and turnaround. Forecasts remain estimates until tested against actual usage.
The full cost of useful work
01
Tokens, context, retries and model choice.
02
Products, compute, storage and hardware.
03
Maintenance, monitoring and human checking.
04
Configuration, integration and custom work.
Evaluate cost per successfully completed task.
Keep quality and turnaround alongside the cost. Separate one-time implementation from ongoing operation.
04 / Support your team
Your team may already have the tools and technical capability. We help resolve the decisions holding things up and agree where outside guidance, implementation capacity or ongoing support would be useful.
Discuss support for your team →Documented decisions, recommendations and scoped support or improvement work, with owners and a review cadence.
The team can act on the decisions. Responsibilities and any ongoing support coverage are explicit.
Client work / data foundations
101, Ryan Cockerill’s real-estate investment firm, needed AI to work with its incoming deal information. We built the ingestion pipeline and database around that existing flow of data, creating a reusable foundation for agents.
See our work and its scope →Delivered foundation
This example demonstrates data-foundation work. It does not establish a measured financial return or a private-model deployment.
Ways to work together
A defined project can move straight to a scoping conversation. These engagement options are not a compulsory sequence. We agree deliverables, timing and fees before work begins.
The paid Blueprint: a focused review of your goal, current setup and requirements, with evaluated options and a practical next step. No commitment to a build.
From $5,000
Scope and timing agreed before work begins
Experienced support for the decisions your team needs to make: what to use, what to build, what to spend and how to evaluate the result.
Custom scope
Project-based or ongoing
Substantial implementation projects, from integrating existing products to developing the components your requirements call for. We agree what will be delivered, tested and handed over.
From $25,000
Milestones agreed for the implementation
Move or adapt the systems that are blocking your AI project. Start with dependencies, continuity and data integrity, then agree an appropriate migration approach.
Custom scope
Scoped per system
Keep an implementation useful as your business, usage and available tools change. Support coverage and responsibilities are defined in the engagement.
From $5,000/mo
Agreed scope and review cadence
Optional / free
An AI-guided assessment helps you describe the goal, current setup and constraints. It produces a first-pass summary and proposed next step, reviewed before the recommendation is shared. It is separate from the paid Blueprint and does not validate costs or commit you to a build.
Start the free assessment →An account saves your progress. Share a high-level description; leave out confidential documents and credentials.
Clear expectations
Our proposal separates our fees from model usage, software, hosting, hardware and ongoing support. We document assumptions, exclusions and who pays each provider. A smaller configuration or optimization request is scoped individually; the Full Build starting price does not automatically apply to every request.
The agreement specifies rights to custom deliverables, control of accounts and access to documentation. Provider software, model licenses and other third-party dependencies retain their own terms. We explain material portability limits before you commit.
Not necessarily. We assess the information and access requirements first. Commercial products, privately hosted models and on-premises deployments have different capabilities, costs and responsibilities. The choice follows your requirements.
We agree the working output, representative test conditions, quality requirements and acceptance owner. Documentation, team handover, maintenance and support responsibilities form part of the scope.
We can implement agreed controls and documentation. Your organization retains its policy and legal decisions unless responsibilities are explicitly assigned otherwise. Testing an implementation is not compliance certification; support coverage and escalation are agreed separately.
Start a conversation
Tell us what you want to achieve and what needs to be true for it to work—your budget, your data requirements, your existing systems or your team’s capacity.
Discuss your AI projectWe’ll discuss whether we’re the right team to help and what a sensible next step could look like.