Questions about Prolego

Who is Prolego?

Prolego builds and operates reliable AI to help companies re-engineer business economics. Founded in 2017 by Kevin Dewalt and Russ Rands, we are an independent boutique that has worked exclusively in AI for major companies across industries. See company history and leadership.

What do you help clients accomplish?

We help clients re-engineer the economics of their business with AI. We combine strategic judgment and delivery so the client gains a system, a repeatable method, an AI-native architecture and experience working as an AI-native product team. See business reinvention.

Do you build the systems?

Yes. Engineering is how the strategy becomes real and how we learn what the business should do next. We help choose the work, build reliable performance and establish a path into operation. See working with Prolego.

What is your track record?

Our selected accounts include policy chat in production across a national branch network, a Coach rollout, two FAA-related engineering workflows in production, an FDA application workflow delivered into production in three months and compliance components handed to a bank's internal team. We put an NLP transformer into production in life insurance in 2019. The full case collection also covers insurance, regulatory strategy, automotive, pharmaceutical and defense work. It is a selection from the wider practice.

How do you differ from a development team?

If you're just looking for a company to build engineering solutions (and that is probably not what you actually need), we're not the right company.

We're here to help you re-engineer the economics of your business with AI. That starts with figuring out where to invest, how to build, how to scale and how the organization needs to change to adopt it.

Our Forward Deployed Engineers (FDEs) connect that strategy with building and operating reliable systems alongside your team. See our FDE model and fit and alternatives.

We already have an AI team. Can you still help?

Yes. A capable internal team can make collaboration more effective. Specialists can add focused experience, delivery capacity or a method for a difficult problem. Agree the contribution and responsibilities alongside the internal team. See internal-team collaboration.

Do you work with private equity firms?

Yes, we help private equity firms identify AI leverage across existing portfolios, assess opportunities in target companies and develop detailed portfolio analyses. We also build and operate AI inside portfolio companies; see AI enablement for private equity portfolios.

What does an engagement cost?

For budgeting purposes, $250,000 to $500,000 is a good starting point for an engagement. We generally use a fixed-team retainer at a fixed monthly fee. Priorities can be redirected without a change order for every request. The proposal defines the team, starting result, sequence, dependencies, client participation and specific commercial terms. See engagement practices.

How quickly could we see progress?

Our first-month goal is a performance report that lets product, operations, compliance, security, executive and technical stakeholders understand expected behavior, potential impact and remaining risks. That helps you decide whether to continue, adjust the work or redirect the investment. Pulling risk forward explains the purpose. The proposal defines the delivery sequence and dependencies.

How do you address hallucinations?

We build reliability from the start with representative cases, expert feedback, evidence checks, appropriate tools and failure handling. The system needs to know when to clarify, hold or escalate as well as when to answer. See PDD and reliability.

What does the client contribute during delivery?

Once we choose an opportunity together, we structure the project with you: the business outcome, sponsor and business owner, expert participation, representative work, approved tools and a technical operations counterpart. Production access is needed before deployment. Expert feedback usually sets the pace of reliability work. We work through these delivery responsibilities together; a company name and an initial idea are enough to begin the conversation. See project planning and client responsibilities.

Who owns the resulting work?

The client owns the code, prompts, workflows, guidance, evaluation material and documentation we create, apart from open-source libraries. The work lives in the client's environment from day one. Cancellation terms are negotiated with pricing and commitment, often using a 30-day period but sometimes another period, such as 60 days. The agreement governs, and the client retains completed work. Third-party services retain their own licenses. See ownership and exit.

When should we use a product or another provider?

Use an existing product when it serves the important task and fits the environment, controls and economics. A substantial Prolego engagement may be excessive for a small task, a pure staffing request or a specialty outside our contribution. See fit and provider options.

How do we get started?

Look at where your business makes money, where it spends money and its major workflows. Tell us your company's name and a sentence or two about what you're considering, by email or through our contact form. We'll reply with suggestions about the larger opportunity and an initial view of potential ROI before a call. See how to contact Prolego.

Do you start with a prototype or proof of concept?

We build the actual workflow and evaluation framework together. Separate POCs helped justify expensive software builds. Today, the central risk is agreeing on what the AI should do and ensuring it does it correctly. See the new AI playbook.

How large is the delivery team?

Three responsibilities anchor the team: engagement leadership for product and strategy, engineering leadership for architecture and methodology, and AI systems engineering to build the product. The proposal sets staffing and allocation. Prolego's historical company size of five to 15 people is not the size of an engagement team. See working with Prolego.

Which technology stack do you use?

Yours. We work in the client's approved environment across cloud providers and major models, keeping the client's knowledge and evaluations available as tools change. See architecture.

None by default. We can use a framework when it earns its place. Modern coding tools make components fast to build; reliability is the bigger challenge. A framework can reduce transparency and slow iteration as well as save effort. See reliability engineering.

Can we start several solutions in parallel?

Yes, but starting with one reduces risk while the teams learn the method and establish a production path. We explore other opportunities in parallel and expand from a useful foundation. Priorities and capacity remain explicit. See engagement practices.

What happens after launch?

Keep us operating and extending the systems or take over with your own team. Live review, evaluation and reviewed AI-assisted fixes keep the system improving. Sources and guidance need continuing business ownership. See PDD and handover examples.

What does Prolego leave to other providers?

We reinvent business workflows with AI, including large-scale process redesign. We help change how the business makes decisions and gets work done, using LLMs as the core reasoning engine.

Traditional RPA and automation built around extensive hand-coded business rules are outside our specialty, along with ERP or CRM implementation, standalone legacy-integration programs, broad staff augmentation and general IT operations. We build the connections and software our AI systems need and work alongside the teams responsible for the wider environment. See fit boundaries.