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We design and build SaaS products with AI features your customers can rely on. Search, summaries, drafts, predictions and assistants, connected to your data with the right permissions, tested for quality and planned for security and running costs.
AI features we build into SaaS products
What this service is
Two different things get called AI development. This page is about the first one.
AI is part of what your customers use. Search that understands questions, summaries of their data, drafts, predictions, or an assistant that takes actions in the product.
Developers use AI tools to write and review code faster. Useful, and many teams work this way, but your customers never see it.
Where AI is the core of the value, planned into the product from version one.
New AI features built into your live SaaS product, alongside your code and your team.
Use case discovery
Not every idea needs AI, and very few need a custom model. Pick an example to see how we’d approach it.
“Answer customer questions from our help docs”
“Flag invoices over a set amount”
“Predict which accounts might cancel”
“Summarise long call notes”
“Find similar support tickets”
“Pull fields out of uploaded contracts”
Architecture
We start at the top of this list and only move down when a simpler option can’t meet the need. Each step down adds capability, and also cost, data needs and more to maintain.
Model providers are chosen per project. We compare options on your own examples for quality, speed, price, data terms and where data is processed. Many products use more than one model.
Fits When the logic is clear and must be exact
Predictable and cheap, but can’t handle open ended language
Fits Summaries, drafts, classification and other language tasks
Quick to build. You pay per use and rely on a provider
Fits When answers must come from your product data or documents
Answers stay grounded and current, but quality depends on search and content
Fits Similarity, meaning based search, matching and finding duplicates
Needs an index that stays in sync with your data
Fits When data must stay in your infrastructure, or volume makes API pricing costly
More control, but you run, scale and update the model
Fits When instructions and examples can’t reach a consistent style or format
Needs good training examples, and repeating when base models change
Fits Predictions from your own history, such as scoring or forecasting
Needs enough clean data and ongoing checks as patterns change
AI features
Each one is designed around a job your users already do, with a clear way to see, correct or undo what the AI produced.
Search that understands questions, not just keywords, across records, files and help content.
Good when People can’t find what they know existsShort summaries of long threads, records, calls or reports, shown where the decision happens.
Good when People skim long content to decide somethingDrafted replies, descriptions or reports that a person edits and sends.
Good when Writing is repetitive but needs a human checkTagging, sorting and sending items to the right team, queue or workflow.
Good when Volume is high and categories are clearStructured fields pulled from emails, PDFs and forms, then validated before saving.
Good when Important data arrives in messy documentsSuggested next steps, related items or settings based on context.
Good when Users choose from many optionsChurn risk, lead scores, forecasts or unusual activity alerts from your own data.
Good when You have enough history to learn fromAn assistant that looks things up and completes tasks in your product, within limits and with approval steps.
Good when Tasks span several screens or toolsData and retrieval
Most useful AI features depend on your customers’ data. The model should only ever see what the person asking is allowed to see.
Each account’s data is separated in storage and in search, so one customer’s content can’t appear in another’s answers.
Indexes update when records change or are deleted, so answers don’t rely on stale or removed content.
We document which data goes to which model provider, and check their retention and training terms against your policies.
Personal or secret fields can be removed or masked before anything reaches a model.
Core SaaS engineering
In most AI products, the model call is a small part of the code. The rest is the SaaS engineering that makes it usable, billable and reliable.
We build with React, Next.js, TypeScript, Node.js, Python, PostgreSQL, Docker and AWS, and choose AI libraries per project. If you also need the wider product, see SaaS application development.
Evaluation and QA
Normal tests expect one right output. AI output varies, so we test it against a set of real examples and clear criteria, and run those tests again whenever an instruction, model or data source changes.
Matches the expected answer or facts
Uses the sources it was given, and says when it doesn’t know
No data from other accounts, no harmful content
Valid structure your code can use
Response time and cost within the agreed budget
Anything marked for review goes to a person, and the fix becomes a new test case.
Security and privacy
AI features add new risks on top of normal web security. We use the OWASP Top 10 for LLM Applications as a checklist, and design so that a model that gets something wrong can’t do much damage.
Users or documents can contain text that tries to override instructions. We treat all model input as untrusted, keep instructions apart from content, and limit what the model can reach.
Retrieval follows permissions, secrets never go into prompts, and output is checked for data that shouldn’t be there.
Assistants get only the tools they need. Actions that change data, spend money or contact people need confirmation.
Model output is validated and escaped before it’s shown, saved or used to trigger anything.
We assume a system prompt can be revealed, so nothing secret goes in it and access control lives in code.
Rate limits, size limits and spending caps stop one user or a loop from running up costs.
No method fully prevents prompt injection today. That’s why the design limits what a manipulated model could do, instead of relying on the model to refuse. Privacy reviews also cover what is logged, how long it’s kept and which providers process it.
AI operating costs
Unlike most features, AI features cost money each time they run. Model providers usually charge by tokens, the small chunks of text that go in and come out. We plan for this before launch, so a popular feature doesn’t turn into an unwelcome bill.
Shown as an example mix. More context and longer answers mean more tokens, higher cost and slower responses. Speed also depends on the model you choose.
Smaller, cheaper models for simple steps, and stronger ones only where they make a difference.
Retrieve a few relevant passages instead of sending whole documents.
Reuse results for repeated requests, and cached instructions where providers support it.
Run jobs that aren’t urgent in bulk, which providers often price lower.
Usage caps or credits that match how you price the product.
Show answers as they’re written, so waiting feels shorter.
How it works
You work with one project lead from start to finish. We prove quality on real examples before the full build, and you get a written scope, timeline and price before build work starts.
Your users, workflows and data. We list possible AI features and rank them by value, feasibility and risk.
You choose what to exploreA small prototype tested on your own examples, to see if quality is good enough before a full build.
You review the resultsApproach, model options, data flow, permissions, security and expected running costs. Then a written scope, timeline and price.
You approve the plan and priceHow users start the AI, see results, correct them and give feedback.
You approve the screensThe AI layer and the product around it, with working demos along the way.
You give feedback on each demoEvaluation sets, security checks, load and cost tests, then fixes.
You review the evaluation resultsRelease to a small group first, behind a switch you control, then widen access.
You decide when to open it upWatch quality, cost and errors, and keep improving instructions, data and models.
You set the next prioritiesDeployment and monitoring
Real users ask things no test set predicted, and models change over time. After launch we watch these signals and act on them.
Feedback buttons, flagged answers and regular reviews of samples.
Spend per feature, per plan and per customer.
Response times, timeouts and slow steps.
Provider failures, with fallbacks and alerts.
New versions tested on your evaluation set before switching, and retirement dates tracked.
Good to know
Honest expectations make better products. These hold for every AI project, including ours.
Every model makes mistakes. Good design makes them easy to spot, correct and report.
If rules or normal search do the job better, that’s what we’ll recommend.
Answers are only as good as the content and history behind them.
Providers update and retire models, so behaviour can shift and needs retesting.
More users and longer inputs mean higher running costs, which is why limits are planned early.
We test quality against targets we agree with you, but accuracy in real use, adoption and revenue can’t be promised in advance.
Scope and cost
AI projects vary a lot, so we don’t sell fixed packages. You get a written scope, timeline and price before build work starts. Running costs for models and hosting are estimated separately, because they depend on usage.
Don’t have all of this? That’s fine. Working it out together is part of discovery.
Discuss Your AI SaaS ProductOur work
These projects show the product design and engineering that every AI feature sits on. Client names are withheld, and we’re happy to talk through the details on a call.
Case 01
We took an HR platform from idea to launch, with product design, development and a new website, then SEO to bring in users.
+45%
trial signups
40%
less HR admin time
~11,000
organic visits a month, from search
Case 02
We designed the product, built the app and the website, then grew it with advanced SEO, including AEO and GEO, and Google Ads.
+38%
quiz completion
1.8s
average page load
~20,000
organic visits a month, from a new domain
Case 03
This client had outgrown their website builder. We rebuilt it as a SaaS platform with an admin dashboard, automated emails and a new website.
+40%
member engagement
30%
less admin work
50%
faster page loads
Testimonials
Client names are withheld. Each quote comes from a real client and is shown with their company type only.
[Paste the client's own words here. A short, specific story about what changed after launch.]
[Paste the client's own words here. One or two plain sentences.]
[Paste the client's own words here. One or two plain sentences.]
[Paste the client's own words here. One or two plain sentences.]
Services
This page covers AI features and AI-driven products. If you need something else, one of these may fit better. You can also combine them.
The software your customers log into, with interfaces, backend and APIs, data, accounts and billing, built and run on secure cloud infrastructure.
Fast, search friendly marketing websites for B2B SaaS, built so your team can publish and update pages without a developer.
The first working version of your product, scoped to what your first users need and solid enough to keep building on.
Experienced technical advice on architecture, tech stack, code quality or scaling, before you commit budget to a build.
A dedicated development team working as an extension of yours, with one project lead and regular demos.
FAQ
It’s designing and building SaaS products where AI is part of what customers use, such as smart search, summaries, drafting, predictions or assistants. It covers choosing the right use cases and approach, connecting your data safely, building the product around the AI, and testing, securing and monitoring it.
No. AI-assisted coding is a way developers work. AI-driven product development means your product itself has AI features that your customers use. Developers may use AI tools on any project, but the code still gets human review and testing.
Usually not. Most SaaS AI features work well with existing models through an API, often combined with retrieval over your data. Custom training makes sense when you have a specific prediction task and enough historical data, or when instructions alone can’t reach the consistency you need.
Yes. We review your product, data and architecture, then build AI features into your current product alongside your team, starting with one use case and a small release.
We choose per project. We compare options on your own examples for quality, speed, price, data terms and where data is processed. That can mean a commercial model API, an open weight model you host, or a mix of both.
RAG, or retrieval augmented generation, means finding relevant content in your own data and giving it to the model along with the question, so answers come from your sources. You need it when answers must reflect your documents, records or help content.
That depends on the provider and the settings used. We check each provider’s data use and retention terms with you before anything is sent, choose options that match your policies, and document where data goes.
The risk can’t be removed completely, but it can be reduced and made easy to catch. We ground answers in your sources, show those sources, let the model say when it doesn’t know, test against real examples and keep a person in the loop where mistakes are costly.
We use the OWASP Top 10 for LLM Applications as a checklist. Inputs and documents are treated as untrusted, permissions are enforced in code, the model only gets the tools it needs, output is validated, and risky actions need confirmation.
It depends on the model, how often the feature is used and how much text goes in and out. We estimate running costs during planning, and use model choice, caching and usage limits to keep them in line with your pricing.
It depends on the number of AI features, the state of your data, your security needs and whether there’s already a product to build into. You get a written scope, timeline and price before build work starts.
You do. The code, prompts, evaluation sets and project files belong to you, and we hand over access and documentation.
Book a call and tell us about your product, your users and the AI idea you have in mind. We’ll talk through whether AI is the right fit, the likely approach and what it would take to build.
Prefer email? Write to hello@saasassistant.com
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