B2B SaaS

Your SaaS customers are asking for AI. Here is how to ship it.

Every enterprise prospect now asks what AI features your product has. Generic chatbot wrappers are not an answer they will pay for. What moves deals is AI that works on the customer's own data, their documents, their records, their history in your product.

We build in-product AI features that are grounded in user data, isolated per tenant, and shipped to production, not a demo. A single AI feature typically ships in 6–10 weeks. A full embedded copilot with RAG takes 10–16 weeks.

How we work

Tell us about the AI feature you want to ship.

Who we build for.

Series A–B SaaS companies adding AI features to existing products
Bootstrapped SaaS products that need to differentiate against better-funded competitors
Vertical SaaS platforms serving regulated industries (legal, financial, healthcare)
Internal tool platforms adding natural language interfaces for non-technical users

What we build.

Six specific AI features that ship as production components in your product.

In-product RAG search

Semantic search across the user's own data inside your product: contracts, emails, records, documents. Results are ranked by relevance, not recency. Each user searches only their own data.

AI copilot assistant

A chat interface inside the product, grounded in the logged-in user's data. Not a generic chatbot. It answers questions about what the user has actually uploaded, created, or configured in your product.

Automated workflow AI

Trigger-based LLM processing: classify new records, generate summaries on upload, draft outbound responses, or flag items that need human review. Runs in the background without user interaction.

Document analysis feature

Users upload any document (PDF, Word, spreadsheet) and the system extracts structured data, answers questions, or flags fields against a schema. Works on documents your product did not originally generate.

Data export intelligence

Natural language queries over the user's database. Users ask "show me all contracts expiring in Q3 where the counterparty is in California" instead of writing a filter query. Results are exportable.

White-label AI agent

A branded AI component you license to your customers as a feature add-on. We build it; you put your name on it and charge for it. Used by SaaS companies that want to offer AI without building the infrastructure themselves.

Who this isn't for.

Pre-revenue SaaS products
AI features need real user data and validated workflows to build against. If you do not yet have paying users generating data in your product, AI features will be built on assumptions rather than real workflows. Build the core product first, then add AI to the workflows your users actually use.

Frequently asked questions.

How do you handle multi-tenant data isolation in the RAG system?: Each tenant's documents are stored and retrieved in isolation. We use namespace-level or metadata filter-level isolation in the vector store so that a query from one user never returns chunks from another user's documents. The specific isolation pattern depends on your vector store and scale requirements. We define this during architecture review before build.
Can users bring their own OpenAI key (BYOK)?: Yes, we can architect the system to accept a user-provided API key and route their requests through it. This shifts the model costs to the user and can be appropriate for enterprise pricing tiers. BYOK adds complexity to key management and error handling. We will discuss the tradeoffs on the discovery call.
How long does a typical AI feature take to ship?: A single AI feature (in-product search, a document analysis tool, or an automated workflow) typically ships in 6–10 weeks from contract to production. An embedded copilot with full RAG pipeline and multi-tenant isolation runs 10–16 weeks. These ranges assume your product's existing API is documented and your data model is stable. Undocumented internals or significant data model changes add time.
Do you white-label the AI components?: Yes. The UI components, API endpoints, and any user-facing copy can all be built under your brand. We do not watermark or brand the output. If you need to tell your customers an AI feature was built internally, that is between you and your customers. We do not advertise the relationship unless you want us to.

Embed AI into SaaS →RAG development →SaaS MVP development →

Ready to ship the AI feature your customers are asking for?

Tell us which AI feature you want to add and what data users would be working with. We will reply within one business day with a rough scope and timeline.

Your SaaS customers are asking for AI. Here is how to ship it.

Who we build for.

Series A–B SaaS companies adding AI features to existing products

Bootstrapped SaaS products that need to differentiate against better-funded competitors

Vertical SaaS platforms serving regulated industries (legal, financial, healthcare)

Internal tool platforms adding natural language interfaces for non-technical users

What we build.

Six specific AI features that ship as production components in your product.

In-product RAG search

Semantic search across the user's own data inside your product: contracts, emails, records, documents. Results are ranked by relevance, not recency. Each user searches only their own data.

AI copilot assistant

Automated workflow AI

Trigger-based LLM processing: classify new records, generate summaries on upload, draft outbound responses, or flag items that need human review. Runs in the background without user interaction.

Document analysis feature

Data export intelligence

Natural language queries over the user's database. Users ask "show me all contracts expiring in Q3 where the counterparty is in California" instead of writing a filter query. Results are exportable.

White-label AI agent

Who this isn't for.

Pre-revenue SaaS products

AI features need real user data and validated workflows to build against. If you do not yet have paying users generating data in your product, AI features will be built on assumptions rather than real workflows. Build the core product first, then add AI to the workflows your users actually use.

Frequently asked questions.

How do you handle multi-tenant data isolation in the RAG system?

Each tenant's documents are stored and retrieved in isolation. We use namespace-level or metadata filter-level isolation in the vector store so that a query from one user never returns chunks from another user's documents. The specific isolation pattern depends on your vector store and scale requirements. We define this during architecture review before build.

Can users bring their own OpenAI key (BYOK)?

Yes, we can architect the system to accept a user-provided API key and route their requests through it. This shifts the model costs to the user and can be appropriate for enterprise pricing tiers. BYOK adds complexity to key management and error handling. We will discuss the tradeoffs on the discovery call.

How long does a typical AI feature take to ship?

A single AI feature (in-product search, a document analysis tool, or an automated workflow) typically ships in 6–10 weeks from contract to production. An embedded copilot with full RAG pipeline and multi-tenant isolation runs 10–16 weeks. These ranges assume your product's existing API is documented and your data model is stable. Undocumented internals or significant data model changes add time.

Do you white-label the AI components?

Yes. The UI components, API endpoints, and any user-facing copy can all be built under your brand. We do not watermark or brand the output. If you need to tell your customers an AI feature was built internally, that is between you and your customers. We do not advertise the relationship unless you want us to.

Ready to ship the AI feature your customers are asking for?

Tell us which AI feature you want to add and what data users would be working with. We will reply within one business day with a rough scope and timeline.

Your SaaS customers are asking for AI. Here is how to ship it.

Who we build for.

What we build.

In-product RAG search

AI copilot assistant

Automated workflow AI

Document analysis feature

Data export intelligence

White-label AI agent

Who this isn't for.

Frequently asked questions.

Related

Ready to ship the AI feature your customers are asking for?

Your SaaS customers are asking for AI. Here is how to ship it.

Who we build for.

What we build.

In-product RAG search

AI copilot assistant

Automated workflow AI

Document analysis feature

Data export intelligence

White-label AI agent

Who this isn't for.

Frequently asked questions.

Related

Ready to ship the AI feature your customers are asking for?