A Handy Guide to Creating an AI Low-Code Platform RFP Template

TL;DR

Write every requirement as a one-line question your evaluation team can score.

• Use six sections in this order: company overview, low-code capabilities, AI integration, integration and data, security and governance, pricing.
• Ask every vendor for certification evidence, such as SOC 2 and ISO/IEC 27001, plus GDPR and HIPAA where your data requires them.
• Ask one AI question generic templates miss: confirm vendor models are not trained on your proprietary data or prompts.
• Score responses with a weighted matrix and a proof-of-concept gate before you shortlist any vendor.

An AI low-code platform RFP template is a structured document for evaluating vendors that provide low-code tools integrated with artificial intelligence. A request for proposal, or RFP, is a formal document that outlines a project’s details, requirements, and objectives. It invites vendors to submit proposals detailing how they will meet those requirements, and it standardizes responses so you can compare vendors side by side.

The difference now is the AI layer. A template written for low-code alone will not surface the questions that separate a platform where AI generates governed, inspectable output from one where it does not. This guide gives you a six-section framework, the AI-specific questions most RFPs still omit, and a scoring approach that holds up under audit.

What belongs in an AI low-code platform RFP template?

Six sections cover what a buyer needs to evaluate. Keep them in this order so your document is faster for a vendor to answer and easier for your team to compare.

Write each requirement as a labeled, one-line question. That format keeps proposals consistent and gives your evaluation team a clean row to score.

1. Company overview and objectives

State why you are buying and what success looks like before you list a single feature.

• Business problem: describe the operational friction you need the platform to fix, such as approvals still running on email and spreadsheets.
• Objectives: name the outcomes and the target go-live date.
• Target users: define the split between business users building under IT governance and professional developers extending the platform.
• Scope: list the departments and processes in scope, such as procurement approvals, HR onboarding, or IT service requests.

For enterprise planning, this guide to application lifecycle management in low-code shows how IT teams scale programs with stronger architecture and governance.

2. Low-code and visual development capabilities

This section tests how apps get built and by whom.

• Visual development: drag-and-drop UI builders, visual logic and workflow modeling, reusable components.
• Who can build: can the majority of apps be built by business users on a platform IT still governs?
• Readability: can a non-developer read the artifacts, or does the output need a developer to explain it?
• Change model: can you change an approval rule directly, or does every change wait for a release cycle?
• Pro-code extensibility: what SDK or API lets developers extend forms, app behavior, and custom UI?

For architecture planning, this guide on building composable enterprise applications with low-code platforms covers scaling under governance.

3. AI and generative integration

Ask how the platform builds with AI and who reviews what it produces.

• App generation: can a single prompt or an uploaded requirements document generate a working app?
• LLM flexibility: which large language models (LLMs) are supported, and can you bring your own model?
• Retrieval: does the platform support retrieval-augmented generation (RAG) against your own documents and data?
• Prompt tooling: what controls, guardrails, and prompt management does the platform provide? -Data privacy: confirm the vendor’s models are not trained on your proprietary data or prompts.
• Billing: is AI included in the platform, or metered as consumption or token-based usage?

4. Integration, data, and architecture

A platform that cannot reach your systems of record will not survive procurement.

• Connectors: what pre-built connectors exist for SAP, Oracle, Salesforce, and Workday?
• APIs: does the platform expose REST and GraphQL APIs for custom integration?
• Deployment: what deployment and hosting options are offered, and in which data-residency regions?
• Data portability: can you export your data and app definitions in standard formats such as JSON and CSV, with no lock-in?
• Environments: what development and production environments exist, and how is promotion controlled?

5. Security, governance, and compliance

Governance is what makes speed safe at enterprise scale, so give it a full section of its own.

• Certifications: does the vendor hold SOC 2 and ISO/IEC 27001, and what evidence does it give for GDPR and HIPAA?
• Access control: is role-based access control (RBAC) enforced on IT-defined permission sets?
• Identity: is single sign-on supported through SAML and OAuth?
• Audit trails: are audit logs platform-native, with every action timestamped and attributed?
• Governance model: is governance built into the architecture, or is it a deliberate setup after deployment?

Map each certification to the controls it covers, and ask for the evidence behind every answer.

Ready to see how governed app generation answers these questions in practice? See how Kissflow approaches enterprise governance built for the pace of AI app development.

6. Pricing and commercial terms

Close on cost and support so the commercial comparison is objective.

• Pricing model: is it per-user, per-app, or consumption-based, and how does it behave at scale?
• Total cost of ownership: what are the implementation, administration, and add-on costs over three years?
• Support and SLAs: what support tiers, response times, and uptime SLA does the vendor commit to?
• References: request customer references and a formal quotation.
• Roadmap: ask for the product roadmap stated as direction, with dates where committed.

AI governance and inspectability: the section most RFPs miss

An RFP that only asks whether a platform has an LLM leaves the hardest questions for after you sign. When AI writes code, a later edit can quietly break steps that relied on the old logic, and explaining the result to compliance can take a developer. Add a section that asks how each platform prevents that before you buy.

• Inspectable output: is every artifact a standard component a developer can open and read, or does it need a developer to explain it?
• Deterministic behavior: does the same prompt produce the same output, and can a reviewer read what it produced?
• Unified audit: do AI-generated changes flow through the same audit log as manual edits?
• Metered or included: is AI included, or metered as consumption billing that makes cost unpredictable?
• Governance by default: is governance platform-native, or does it depend on the buyer configuring it correctly?

Kissflow, an AI-powered enterprise application platform, answers this section with blueprints: structured metadata that describes business logic. That output is deterministic and inspectable, and AI changes use the same audit log as manual edits. AI Builder, generally available as of May 2026, turns a single prompt or an uploaded requirements document into a complete working app, refined through conversation with a human in the lead.

Two customers have tested that against named alternatives: Godrej Consumer Products displaced Mendix with Kissflow, and Aswaq Management and Services evaluated Power Apps and Creatio before choosing Kissflow. For the security section of your RFP, Kissflow’s certification and compliance list covers SOC 1 Type II, SOC 2 Type II, SOC 3, ISO/IEC 27001, HIPAA, GDPR, and CCPA, and it offers a 99 percent uptime SLA.

How do vendors compare on who can build and how output is governed?

Two questions decide an AI low-code selection: who can build the apps, and whether IT can govern the output. Evaluate every vendor on the same axes, and require published figures. Where a vendor publishes nothing for an axis, record that plainly.

Each of these platforms has genuine strengths: OutSystems and Mendix suit professional development teams, and Power Apps integrates deeply with Microsoft 365. The RFP surfaces where they differ on who can build under governance and whether AI output stays inspectable.

How should you score and shortlist vendor responses?

A scorecard turns opinions into a defensible decision. Build an evaluation team of the stakeholders the project will directly affect, weight each section by what matters to your program, and run a proof-of-concept gate before you shortlist.

Treat the example weights as a starting point and move them to match your program.

Run a short proof of concept against a real workflow, such as a procurement approval, before any vendor reaches the shortlist. A working app under your governance rules tells you more than a demo does. After scoring, review contracts and scope of work with the shortlisted vendors to close open questions.

What timeline should an AI low-code RFP follow?

Clear dates let vendors self-select and keep your evaluation honest. Publish the schedule inside the RFP so every vendor works to the same clock.

• RFP issuance: send the same document to every vendor on the same day
• Vendor questions due: leave room for questions before vendors write, and share every answer with all of them
• Proposal submission deadline: one date for all, with late proposals out
• Vendor demos and proof of concept: one real workflow, built under your governance rules
• Final selection and award: scores locked before contract talks begin

For scale planning, see the guide to the enterprise low-code platform.

Frequently asked questions

What is the difference between an AI low-code RFP and a standard low-code RFP?

A standard low-code RFP evaluates visual development, integration, and pricing. An AI low-code RFP adds a dedicated section for LLM flexibility, retrieval-augmented generation, data-privacy guarantees, and whether AI output is inspectable and governed. The AI section shows whether AI output stays inspectable and governed after the vendor leaves the room.

What AI-specific questions should an RFP include?

Phrase each AI question so the vendor has to show evidence: the contract clause that rules out training on your data, the list of supported models, the line on the order form where AI usage is billed, and a sample audit log entry for a change the AI made.

How long should an AI low-code RFP template be?

Long enough to cover the six sections and short enough that vendors answer it fully. Keep the core RFP focused and let the scoring matrix and a proof-of-concept gate do the heavy comparison. Depth in the AI and governance sections matters more than total page count.

Which compliance certifications should the RFP require?

Require SOC 2 and ISO/IEC 27001 as a baseline, plus GDPR and HIPAA where your data demands them. Ask for role-based access control on IT-defined permission sets and platform-native audit trails. Treat certifications as evidence to verify, and request the current attestation reports from each vendor.

Can business users build the apps, or do you need developers?

Ask the vendor to state what proportion of apps a business user can build without code, whether a non-developer can read the resulting artifacts, and how IT retains governance over what gets built. On a governed platform, business teams build, IT governs what they build, and developers extend it when the logic gets hard.

Where to start

Draft the company overview and objectives first, because every later section is scored against them. Send every vendor the AI governance questions in the same words, so the answers line up row by row on your scorecard.

Book a demo to run your RFP’s AI governance questions against a Kissflow build of one of your own workflows.

Kissflow Low-Code

Get in touch with us to see how Kissflow’s AI Builder can meet your application needs.

Get Started