Custom AI inside existing products
AI features added to your SaaS without rebuilding: copilots, semantic search, content generation, agents. Built as a parallel AI layer that talks to your existing APIs.
Service
AI Integration for Existing Software
When off-the-shelf vendor AI doesn't reach into your business.
We build custom AI that reaches into the software you already run, for when vendor AI features from Salesforce, Notion, or Microsoft Copilot cannot touch your specific data, workflows, or compliance environment. A parallel AI layer that lives alongside your existing systems.
WhatsApp Business integrations →Marketing Automation →for Claude specifically, see Built with Anthropic Claude →for OpenAI specifically, see Built with OpenAI →
Where we focus.
Six application areas where this service ships measurable results, chosen against the failure modes most growing businesses hit.
AI for non-Microsoft, non-Salesforce stacks
If your business runs on industry-specific or custom tools, vendor AI doesn't reach you. We build the AI layer that does.
Compliance-aware AI integrations
When SaaS vendors can't operate in your compliance environment: financial services, healthcare, government, we build custom on-prem or compliance-cleared cloud.
Semantic search & retrieval over your data
Search across your CRM, docs, tickets, and product data with grounded, cited answers. Built with your access controls, not a public LLM's defaults.
AI inside customer-facing portals
Assistants and agents embedded in your existing app or portal: onboarding help, self-serve support, in-product recommendations, without touching your core monolith.
Legacy modernisation with AI
Extend monolithic ERPs, claims systems, and admin tools with an AI layer that wraps the legacy core. Adds value without a multi-year rewrite.
Reality check
Vendor AI stops at the edge of your stack
Every platform you run has shipped an AI feature, and each one works beautifully inside its own boundary. Copilot understands your Microsoft documents. Einstein understands your Salesforce records. Neither can see the industry-specific system that actually runs your operation, and none of them will read across all three.
This is the same finding MIT reached from the other direction: AI initiatives fail on the complexity of connecting to the real systems of record, not on model quality. Vendor AI does not solve that, because a vendor will only ever reach as far as its own data.
The work is unglamorous and it is the whole job: reading from systems whose APIs were designed for something else, respecting permissions that already exist so the assistant never shows someone what they should not see, grounding answers in your data so they can be cited, and doing it without touching the core system everyone is afraid to change.
Related work
Where we've shipped this in production.
SOCIAL NETWORKING · UK 
CONSUMER HEALTH · UK 
REAL ESTATE · WHATSAPP AI
Nuancers
Robo-advisory matchmaking layered onto an existing social product: AI integrated without rewriting the host app.
Read case study -> EDTECH · NETHERLANDSSigma ALS
AI classroom assistant integrated into a Dutch K-12 platform: research-backed, language- and curriculum-aware.
Read case study -> EDTECH · USArusto AI
Design partnership for an AI learning platform: the integration patterns and product surface for an AI-first edtech.
Read case study -> CONSUMER HEALTH · UK Gluf
How KUMO built Gluf, an AI-powered gluten-free restaurant discovery app for the UK coeliac community. Award-shortlisted.
Read case study -> REAL ESTATE · WHATSAPP AI Klickie
How KUMO built Klickie, an Antler-backed WhatsApp-native AI SaaS for property listings and visit booking.
Read case study ->What it costs
What this costs
The short answer
Adding production AI to software you already run starts at $20K to $50K over 4 to 16 weeks, built as a layer alongside your existing systems rather than a rewrite. Larger multi-system work runs $50K to $100K. If your existing vendor's AI feature genuinely covers the use case, we will tell you on the call.
Starter Build
$20K to $50K
4 to 16 weeks
A first production build, shipped end to end with evaluation and human review built in.
Grow Build
$50K to $100K
16 to 24 weeks
Multi-workflow or multi-feature platform work, re-engineered for scale.
Support and Growth Team
$5K to $10K per month
Ongoing, cancel with 30 days notice
The engineering team on retainer once it is live: new work, evals, and monitoring.
These are our three live engagement bands, the same across every KUMO service. Not sure which one fits? That is exactly what the first call is for.
Technologies and platforms
Assembled for your build.
FAQ
My SaaS already ships AI features. Why would I need custom? +
We build custom when the AI needs to reach deeper into your specific data, workflows, and compliance environment than a vendor's defaults allow. You get AI that works on your terms and gives you an edge competitors running the same SaaS cannot copy.
How do you add AI without rewriting our existing code? +
Parallel AI layer that talks to your existing APIs and database. New AI features run in their own services. Your existing code is untouched unless we are adding a UI surface.
Will adding AI slow down our existing features? +
No. AI services run separately with independent scaling. AI calls happen async or behind feature flags.
How do you handle customer data privacy? +
Privacy is architectural. Per-customer isolation, role-based access, data masking before anything sensitive touches an AI API. For regulated industries we deploy on-prem or in confidential computing environments.
What does a typical integration engagement look like? +
A single AI integration takes 8-12 weeks end to end: discovery, build, eval, and rollout. Multi-feature builds run 12-16. We sit alongside your existing engineering team in your sprint cadence and hand the integration over with documentation and runbooks at launch.
How much does AI integration cost? +
A first integration runs $20K to $50K over 4 to 16 weeks. Multi-system work runs $50K to $100K. Ongoing engineering is $5K to $10K per month.
Why not just use our vendor's built-in AI? +
Use it where it fits. It stops at the boundary of that vendor's own data, so if your question spans a custom system, an ERP and a document store, no single vendor feature can answer it.
Do you have to change our existing system? +
Usually not. Most of this is built as a parallel layer talking to existing APIs, which is what makes it safe to ship against systems nobody wants to touch.
How do you handle permissions and access control? +
We inherit yours. The AI layer respects the access rules already in your systems, so a user never sees through the assistant what they could not see directly.
Tell us what you're solving for.
We'll listen first, ask the right questions, and follow up with a clear proposal.