AI features for SaaS products that customers use every day
We add copilots, smart search, summaries and recommendations to the software your customers already pay for, working inside your codebase, your data model and your security rules.
AI summary
Usage up 14% this week. Three accounts show churn risk.
- To a working prototype inside your own product
- 3–4 weeksTo a working prototype inside your own product
- Each customer’s data stays isolated, in retrieval and in prompts
- Per tenantEach customer’s data stays isolated, in retrieval and in prompts
- Model cost tracked, so you know your AI margin
- Per featureModel cost tracked, so you know your AI margin
Your customers expect AI. A chat box won’t cut it.
Buyers now ask what your product does with AI, and competitors have a slide for it. Bolting a generic chatbot onto the sidebar rarely helps: it doesn’t know the customer’s data, can’t take action in your app, and adds a model bill nobody planned for. AI features for SaaS products work when they sit inside an existing workflow and make one job faster.
- AI is on the roadmap, but your team is fully booked on core features
- A demo works on sample data and falls apart on real customer accounts
- Security questionnaires now ask how you isolate tenant data in AI features
- Nobody can say what each AI request costs or whether it’s worth it
AI features for SaaS products, built into your stack
We pick the feature with the clearest value, build it against your real data model and APIs, and ship it behind a feature flag. Every feature comes with an evaluation suite, tenant isolation, usage limits and cost tracking, so you can roll it out with confidence and price it properly.
In-app copilots that act
Users ask in plain language and the copilot calls your own API to create, update or report, under that user’s permissions, with a confirm step.
Smart search
Semantic and keyword search across records, documents and notes, so users find what they mean, not just what they typed.
Summaries and insights
Account, case or project summaries generated from activity history, with links back to the underlying records.
Recommendations
Next best action, similar items or likely answers, ranked from your product’s own usage data.
Tenant isolation and guardrails
Retrieval is scoped to the tenant and user, prompts are protected against injection, and outputs are checked before they reach the screen.
Evaluation and cost control
Automated tests on real scenarios, model routing and caching to keep cost per request down, and dashboards for usage, latency and spend.
AI features that customers actually use
Copilot for a field service platform
Dispatchers describe a job in a sentence and the copilot drafts the work order, picks the parts and proposes a technician for review.
For: Vertical SaaS for field service
Search across case files
Users find the right precedent, document or note across thousands of records by describing it, with filters still applied.
For: Legal, insurance and compliance software
Account and case summaries
A one-paragraph brief of everything that happened on an account this quarter, generated when the user opens it.
For: CRM, customer success and support tools
Ask your data in plain language
Users type a question about their metrics and get a chart and a short explanation, built from queries against their own tenant’s data.
For: Analytics and reporting products
Product recommendations
Similar items, bundles and reorder suggestions ranked from purchase history and catalogue data, tested against current rules.
For: E-commerce and B2B ordering portals
Drafting assistant
First drafts of proposals, replies or reports in the customer’s tone, using the facts already in your product.
For: Sales, HR and professional services software
How we ship an AI feature into your product
- 01Week 1
Pick the feature and the metric
We review your product, data model and usage, choose one feature, and agree how success will be measured: adoption, time saved or tickets avoided.
- 02Week 2–4
Build a working prototype
The feature runs in your staging environment on realistic data, so your team and a few customers can try it and we can score it.
- 03Week 5–8
Harden for production
We add tenant isolation, guardrails, rate limits, cost tracking, monitoring and tests, and support your security review and customer questionnaires.
- 04Week 9 onward
Roll out and measure
The feature reaches customers behind a flag, cohort by cohort. We track usage, quality and cost, and tune it every month.
What you get
- A production AI feature in your codebase and cloud
- Model gateway with routing, caching and usage limits
- Tenant-scoped retrieval and permission checks
- Evaluation suite built from real product scenarios
- Usage, quality and cost-per-tenant dashboard
- Answers for customer security questionnaires
- Privacy impact assessment under PIPEDA and Law 25
- Documentation and hand-over to your engineering team
What it’s built with
- Models
- ClaudeGPTGeminiCohereMistralOpen-weight models
- Your stack
- TypeScript and ReactNode.jsPython.NETRuby on RailsiOS and Android
- Infrastructure
- AWSAzureGoogle CloudPostgres with pgvectorRedisOpenTelemetry
Frequently asked questions
Show us your product. We’ll suggest the first AI feature.
In a free 30-minute call we’ll look at your product and users, name the AI feature most likely to get used, and tell you what a prototype would take.
30 minutes with a senior engineer. No sales pitch, no obligation.
