AI Strategy and Implementation Consulting: Where to Start

Where to start when you want AI but have no plan

Most companies arrive at AI with pressure from the board, a list of vendor pitches and no way to compare them. The failure mode is predictable: a chatbot nobody uses, a pilot that never left the demo stage, or a tool subscription that duplicates what the team already does. Tepia’s AI strategy consulting exists to replace that with a short, structured decision.

The starting point is not the technology. It is a list of the places in your product and operations where a person reads, decides, writes or searches repeatedly. Those are the candidates. Tepia then scores each one on value, feasibility and data readiness, and recommends one pilot you can measure in weeks, not a transformation program you will measure in years.

Tepia does this as a product engineering studio, not a slide deck consultancy. The same US based leads who run the assessment scope and deliver the pilot, which keeps the recommendations honest. If you already know what you want to build, skip ahead to AI integration services or custom AI agent development.

Data readiness and build versus buy

AI projects fail on data more often than on models. During the assessment Tepia inventories the data each use case needs, where it lives, who owns it, how clean it is and whether you are allowed to use it. The data readiness report rates each source and lists the fixes needed before a pilot can start, which is often a few weeks of export, cleanup and permission work rather than a data platform project.

Build versus buy is the second decision. Tepia’s position is that you should buy when a packaged tool covers the workflow well, uses only non sensitive data and does not touch your differentiation, and build when the use case depends on your own data, sits inside your product, needs integration with your systems or would hand a vendor something proprietary. The custom AI versus off the shelf tools page has the full decision matrix.

Privacy and compliance are assessed alongside. Tepia documents which data may go to a hosted model API, which must stay in your AWS, Azure or GCP account with open models, and what HIPAA, GDPR or CCPA obligations apply. For healthcare clients Tepia plans HIPAA aware architecture with BAAs and audit logs from the first pilot.

How Tepia approaches AI strategy and implementation consulting

The assessment is Tepia’s Discovery phase applied to AI. Tepia runs a system investigation of your product, data stores and integrations, interviews 6 to 12 stakeholders and front line staff, and reviews third party tools and model vendors already in use.

The pilot then follows the remaining phases in compressed form. Design produces wireframes and sample designs for the user facing part of the feature, including how uncertainty and errors are shown. Development and Testing builds the feature behind a feature flag, assembles an evaluation set of real cases, and runs Alpha and Beta releases with a written test plan. Training covers the admin tooling and how staff review AI output.

A Tepia project manager is assigned throughout, with US based design and engineering leadership and engineers working in US overlapping hours. The full six phase process is at tepia.co/process.

What a successful first AI pilot looks like

Tepia defines success before the pilot starts, in one or two numbers: minutes per ticket, first response time, proposals drafted per week, search success rate, or documents processed per hour. The pilot is run against that number, reported honestly, and either expanded or stopped.

A typical sequence Tepia sees over a year is an internal pilot in the first quarter, a second internal use case and an improvement release in the second, and a customer facing feature in the third once the data and the team are ready. Tepia’s apps average 4.5 stars on the App Store and Google Play, and the AI features Tepia adds are designed to protect that rating rather than gamble with it.

Tepia has thirteen years of disciplined engineering behind this work and builds the full stack around AI: mobile, web, backends and integrations. See examples at tepia.co/our-work, the service list at tepia.co/services, or the guide how to integrate AI into an app.

Frequently asked questions