Implementation done with your staff in the room — not behind a curtain. Flat-fee engagements, vendor-neutral architecture, and capability transfer built into every deliverable. Your team owns what we built when we leave.
“Implementation” in AI work covers a wider span than the word suggests. A practical implementation engagement typically includes architecture decisions, data pipeline build, model selection and evaluation, prompt and retrieval engineering for LLM applications, integration with the systems AI will affect, production deployment with observability, governance instrumentation, and the documentation that lets your team operate the system after handoff. We do all of it — but we do it in working sessions your team attends, not behind a delivery firewall.
The specific scope depends on what you’re building. A retrieval-augmented LLM application for an internal help desk looks different from a classical ML model for fraud detection, and both look different from an intelligent automation pipeline replacing manual document processing. The engagement starts with a scoping conversation; the proposal is shaped to your specific situation.
The honest answer is that most mid-market and mission-driven organizations should build with help for the first one to three serious AI capabilities they put into production, then operate without help thereafter. The reason is not that AI implementation is impossibly hard — it isn’t — but that the first projects are where the costly mistakes happen, and the senior practitioners who would prevent those mistakes are scarce and expensive to hire full-time.
Hiring a Chief AI Officer or VP of AI before you have a project running is a category of mistake we’ve seen repeatedly. The role definition is hard to write without an in-flight project to ground it; the leader spends their first year doing strategy work that would have been cheaper to engage; and the team they hire arrives before the systems exist for them to operate. If you’re considering an AI leadership hire as a precondition for implementation, our sister firm Talent Echo Advisory Group can advise on the role definition and timing — and often the right sequence is implementation first, leadership second.
Implementation engagements at A2 are flat-fee and milestone-paid. The standard arc is twelve to twenty weeks, with a first production deployment in weeks eight to ten. The engagement is led by a senior practitioner from kickoff to handoff. Your team is in the room for every working session — not the readouts, the sessions.
The four phases of a typical implementation:
Phase one, weeks one to three: architecture and integration design. We finalize the technical architecture, identify integration points with your existing systems, define the evaluation harness and success metrics, and write the governance instrumentation plan. Your security, data engineering, and operations teams are in these sessions because the decisions live with them afterward.
Phase two, weeks three to eight: build and iterate. The core build. Data pipelines, model evaluation, prompt and retrieval design, integration scaffolding, observability instrumentation. Working sessions are co-led; your engineers see and write the code alongside our practitioners.
Phase three, weeks eight to twelve: production deployment and hardening. First production release. Live evaluation against the harness. Bias and edge-case testing. Documentation pass with your team writing the operational sections. Stakeholder review with the business owners and compliance team.
Phase four, weeks twelve to twenty: extension, hardening, and handoff. Additional capabilities, edge-case handling, and the structured two-week handoff at the end. After handoff, your team operates the system. We’re available for defined post-engagement support, but the point of the engagement is that you don’t need us.
We do not bring a stack. We use yours. If your organization is standardized on AWS, we build on AWS. GCP, Azure, on-premises, hybrid — whatever your data and security teams have already committed to. The same applies to model providers: OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure OpenAI, open-weight models running on your infrastructure. We make recommendations based on what fits the specific problem, but the choice is yours and we work with your existing commitments.
The reason is operational, not philosophical. AI implementations that depend on a vendor stack you didn’t already own become a permanent operational liability. Every model update, every contract renewal, every internal staffing change touches the system we built. Vendor-neutral architecture means the system stays in your control after we leave — which is the only way capability transfer actually works.
Implementation is the right next step if you can describe, in concrete terms, what business outcome you’re trying to move, what data supports that outcome, how the system will fit your existing operations, and who will own it after handoff. If those answers are clear, we can scope an implementation engagement directly.
If those answers are unclear, the right next step is an AI strategy engagement first. Trying to skip to implementation when the strategy isn’t done usually costs more, takes longer, and produces a worse outcome. About a third of our strategy engagements end without an implementation, because the strategy work surfaces a cheaper path or a reason not to build yet. We’d rather you find that out from us than from a sunk cost six months in.
Flat fee, scoped to the engagement, paid in thirds: at kickoff, at first production deployment, at handoff. We do not charge hourly. The fee does not inflate during the engagement. We do not take success fees or percentages of project budget.
The specific price is shared in the first conversation, after we’ve understood the scope. The first conversation is free.
Architecture and pipeline build, model selection and evaluation, prompt and retrieval engineering for LLM applications, internal AI tooling and team copilots, production deployment with observability, governance instrumentation, and documentation co-written with your team.
Yes. We work vendor-neutral: AWS, GCP, Azure, on-premises, hybrid. Model providers include OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure OpenAI, and open-weight models. We do not bring a proprietary stack and we do not leave you locked into one.
Twelve to twenty weeks for most engagements, with first production deployment in weeks eight to ten. Faster is possible for narrower scope; longer is rare and usually signals the work should be split into multiple engagements.
With you. Your team is in every working session as co-builders, not stakeholders being briefed. Documentation is co-written. When the engagement ends, your team owns and can extend what we built. This is the central difference between A2 and the default consulting model.
We work with the team you have. Often your most valuable co-builders are not AI engineers but the domain experts and software engineers who own the systems AI will integrate with. We pair our practitioners with them. The goal is capability transfer to whoever will operate the system after handoff — that might be your existing platform team, a new team you’re building, or a hire we surfaced together with our sister firm Talent Echo.
The first conversation is 30 minutes, free, and intended to figure out whether implementation is the right next step or whether strategy comes first.
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