Writing a Technical Brief for an AI Agency

A strong technical brief tells an AI development agency what problem you're solving, what data and systems you have, what success looks like, and what constraints they're working inside. It should run 4 to 8 pages. Anything shorter produces vague proposals. Anything longer usually means the brief is doing the agency's discovery work for them, which is not your job.

This post is for SaaS, EdTech, and FinTech founders who are ready to engage an AI product development agency and want proposals they can actually compare. Not generic guides about "defining your vision" that could apply to building a mobile app in 2014. AI projects have a specific set of inputs that affect scope, cost, and timeline in ways that a standard software brief doesn't account for. Get this wrong, and you'll spend three weeks in back-and-forth before any real work starts, or worse, you'll get a proposal based on assumptions that fall apart in week two of the engagement.

A technical brief for an AI product is not the same as a feature specification. You're not just describing what you want the product to do. You're telling the agency what raw materials they have to work with, what integrations already exist, and where the uncertainty lives. Agencies price against uncertainty. The more clearly you can map the unknowns, the more honest the number they give you will be.

Start with the problem, not the solution

Most briefs open with "we want to build an AI chatbot that" and then describe a feature set. That's backwards. Before any capable agency cares about features, they need to understand the underlying problem well enough to challenge your proposed solution.

Write two to three paragraphs describing the current state. What workflow is broken? What does it cost your business right now, in time or money or both? For a FinTech company, that might be: loan officers spending four hours manually reviewing documents that should take forty minutes. For an EdTech platform, it might be: tutors writing the same feedback on student assignments repeatedly, with no ability to scale that quality across a larger cohort.

Be specific about the cost. "It takes too long" is not a problem statement. "Our support team handles 1,200 tickets per month and 60% require a senior agent because the AI tool we have can't resolve ambiguous policy questions" is a problem statement. Agencies use this to assess fit. If your problem is in their wheelhouse, they'll say so clearly. If it isn't, a good agency will tell you that in the first call rather than six weeks in.

Describe what you have, not just what you want

This section is where most briefs fall apart, and it's the section that has the most direct impact on your project cost.

AI products run on data and integrations. An agency scoping an AI document processing tool needs to know what format your documents are in, how many there are, whether they're structured or unstructured, where they live, and whether there's a clean API to access them. The difference between a well-structured document store with a REST API and a mix of PDFs in a shared Google Drive can add $30,000 to $80,000 in data preparation and engineering work.

List your systems. CRM, ERP, databases, data warehouses, authentication providers, whatever is in your stack. Note which have documented APIs and which don't. If you're running on Salesforce with a clean API and your customer records are reasonably normalised, say that. If your historical data lives in a legacy system that hasn't been touched since 2019 and the documentation is thin, say that too. Agencies have seen both. They price accordingly.

For AI-specific work, also describe your data. If you want a model fine-tuned on your content, how much labelled data do you have? If you're building an AI assistant that understands your knowledge base, what does the knowledge base look like and how current is it? These details change the architecture and the cost in meaningful ways.

Define success before you describe features

Before the feature list, write a success criteria section. This forces clarity on both sides and prevents the most common source of post-launch disappointment: the agency built what was specified, but it didn't actually solve the problem.

Good success criteria are measurable and tied to the original problem. "The AI accurately classifies support tickets with 85% precision, reducing senior agent escalations by 40% within 90 days of launch." That's a success criterion. "The product feels smart and users like it" is not.

For EdTech products, you might define success as: the AI generates personalised feedback for student writing submissions with a quality score (rated by educators) above 4 out of 5, and the time teachers spend reviewing AI output is under 2 minutes per submission. That's testable. You can build evaluation into the delivery milestones.

Be honest about what you don't know. If you're uncertain what a realistic accuracy target is for a classification model in your domain, say so and ask the agency to inform that target during discovery. That's a legitimate open question for an experienced team to answer. It's not a gap you need to fill before sending the brief.

Lay out your constraints clearly

Constraints shape everything. Budget range, timeline, team availability, compliance requirements, and infrastructure preferences all affect what an agency will propose.

Budget: Give a range. Agencies that won't work with you unless you share a budget are actually doing you a favour. A $40,000 brief looks different from a $200,000 brief, and an agency that quotes you without knowing which world they're in is guessing. For context, most production-ready AI features built by a specialist agency fall somewhere between $35,000 and $150,000 depending on complexity and integration depth. Discovery phases typically run $8,000 to $20,000 separately. If you're exploring different approaches to building your product, you might also want to understand the difference between an agency and a product studio before finalising your budget assumptions.

Timeline: If you have a hard deadline, say why. "We need to launch before our annual conference in October" is a real constraint. "We'd like it done quickly" is not. Real deadlines affect how an agency structures the team and what they prioritise in scope. If you're working with an outsourced team, structuring your sprint correctly becomes critical to meeting those timelines.

Compliance: FinTech founders often underestimate how much GDPR, SOC 2 expectations, or FCA-adjacent data handling requirements add to AI projects. If your product will process personal financial data, that affects model selection, data storage, logging, and audit trail requirements. Note this upfront so the agency factors it into the architecture from the start, not after the first compliance review. For FinTech-specific products, understanding the nuances of product development in the space can help you ask better questions of your agency about compliance integration.

Infrastructure preferences: If your engineering team has strong opinions about cloud providers, or if you're locked into AWS because of existing enterprise agreements, say so. If you have no preference and want the agency to recommend, say that too.

What to include about your team

Agencies want to know who they'll be working with. Not because they're being territorial, but because the integration between their team and yours affects how work gets done and how fast decisions get made.

Include: who the product owner is, how much time they have each week, whether you have internal engineers (and what their capacity is), and who the final decision-maker is on scope and spend. A brief that lists a technical co-founder who can review PRs twice a week is a very different engagement from one where all technical decisions flow through a non-technical founder who is also running sales.

If you've worked with development agencies before, a single sentence on what went well and what didn't is genuinely useful. Agencies read these signals. They tell you something about how to set up the working relationship.

Format and length

Aim for 4 to 8 pages. Use plain language. Headers and bullet points are fine, but the thinking should be in full sentences, not a list of disconnected fragments. Bullet points without context force the agency to guess at what you mean, which produces proposals full of caveats.

Sections to include, in order: problem statement, current state and systems, data inventory, success criteria, features and functionality (this is where the feature list lives, after everything above), constraints, team and stakeholders, and any relevant background on previous attempts.

Do not include: detailed UX wireframes unless they already exist, a technology mandate unless you have a hard reason for it, or a project plan. You're hiring people to think about these things. Let them.

One thing that trips founders up

The brief isn't a contract. It's a starting point for a conversation. The goal is not to specify everything, it's to give a competent agency enough signal to tell you whether they're the right fit, what the broad shape of the work looks like, and roughly what it will cost to find out more.

Founders who try to over-specify the brief before engaging an agency often end up doing unpaid discovery work and then handing it to a team that would have done that work better if they'd been involved earlier. The brief gets you to a credible proposal and a first serious conversation. Discovery is what turns that into a real plan.

If you're not sure whether your brief is ready, read it aloud and ask whether a smart engineer who knows nothing about your business would understand the problem, the materials they have to work with, and what done looks like. If yes, send it.