AI strategy & implementation

We help leadership teams identify where AI is useful, establish governance, organize delivery squads, and integrate the right capabilities into real workflows.

№ 01
Opportunity and readiness

We prioritize the use cases with a credible business case, the right data, and an owner inside the operation.
№ 02
Governance and risk

Decision rights, privacy, review points, evaluation criteria, and human oversight designed before rollout.
№ 03
AI delivery squads

A focused senior team that combines business context, implementation, change management, and measurable outcomes.
№ 04
Workflow integration

Useful AI connected to the systems and channels where your team already works.

AI implementation starts with a business workflow, not a model or a chatbot. We help identify the right opportunity, connect useful capabilities to the systems people use, and define how the result will be evaluated and operated.

What we build

Knowledge assistants that retrieve relevant material from approved sources and help people find and check answers.
Support and intake workflows that help classify requests, prepare answers, and hand off to a person when needed. ChatWise is our support-chat product.
Document workflows that assist with extracting, classifying, and reviewing information, with validation before downstream actions.
Decision-support agents that bring relevant information into operational decisions while preserving human responsibility.

These are types of implementation to assess against your workflow, not guaranteed outcomes. Harvie is a recruitment and applicant-tracking platform, not a sales automation product.

From opportunity to operation

We define the problem and baseline, assess data and access, test a bounded workflow, and agree on the evidence needed to proceed. Integration, team adoption, monitoring, and ownership matter as much as the model. Scope, timing, and cost depend on the systems, risks, and work involved.

What we won’t build

We do not recommend AI just to use AI. If a simpler workflow, integration, or existing tool is the better answer, that is part of the recommendation. We do not promise a fixed improvement, delivery date, or return without understanding the case.

No fixed document count determines readiness. We start with the questions or tasks you need to support, then assess the relevant sources, their quality, access permissions, and coverage. A focused, maintained knowledge base is more useful than an uncurated document dump.

We design around approved sources, explicit limits, evaluation with representative questions, and human review where the consequences require it. Retrieval and citations help users check an answer, but do not guarantee correctness. Actions that change systems need scoped permissions and confirmation.

We'll say so. Discovery may point to process improvement, conventional automation, an existing product, or better integration rather than AI. The recommendation follows the problem.