We add the AI layer to the product you already run — so nothing that works today has to change.
We map where an LLM actually changes a metric — support load, activation, time-to-first-value — and where it does not.
You get: a one-page plan: capability, data map, guardrails and a cost ceiling per 1,000 requests. Fixed $4,900, credited toward the build.
A working slice behind a feature flag: prompts, retrieval over your documents or database, and an evaluation set.
You get: real cost and latency numbers from your traffic, not a demo.
Fallbacks for provider outages, monitoring of quality and spend, and handover documentation so your team owns it.
You get: a live feature, a runbook, and an evaluation harness running in your CI.
Read how we roll out AI features in an existing SaaS product →
Text generation, summarization, classification and extraction inside your existing screens.
Agents that take actions in your product — draft the email, file the ticket, update the record — with an audit trail your customers expect.
RAG means answers grounded in your documentation, tickets or database, with citations — instead of a chatbot that makes things up.
Six patterns cover most first integrations. Each ships as a bounded feature you can measure before you widen the scope.
Tickets classified by intent and urgency, relevant help articles retrieved, replies drafted for an agent to approve.
Faster first response, fewer repetitive tickets
Invoices, contracts and intake forms turned into validated fields; low-confidence ones go to a human, not a guess.
Manual data entry becomes a quick review step
Semantic search across your documents, tickets or catalog; exact identifiers still win when they should.
Users find answers instead of filing tickets
“Why did this invoice fail?” answered from your own data, scoped to what each user is allowed to see.
New users reach value sooner, ask you less
Product descriptions, outreach and report narratives generated from your data, with a review step before publishing.
Catalog-scale copy without agency budgets
Multi-step agents reconcile records and chase missing information across your tools, with approval gates for anything irreversible.
Your ops team does higher-value work
The same pattern — a service boundary, retrieval over your data, a human in the loop — lands differently in each industry we work in.
Transaction categorization, support triage and KYC document extraction with human review. PII stays in your cloud; prompts are logged without card data.
Intake summarization and triage in the spirit of the symptom checker we built for Caidr — always with a clinician sign-off step.
Product descriptions at catalog scale, semantic search, returns and support automation fed by order history.
Outreach drafting, candidate summaries and screening assistants — the pattern behind Sourcd.ai, built for Hiremii.
Summarization, smart replies, moderation and translation without moving message history out of your infrastructure.
We do not sell a platform. We integrate the model that fits your data, budget and compliance needs — through a service boundary your own team can maintain.
If your product runs on something else, the integration pattern is the same — a service boundary and an API.
Client work, named where we may name it.
An OpenAI-powered assistant delivered as a Chrome extension: React front end, Node server on AWS, Auth0. Personalized candidate emails went from an hours-long chore to seconds.
A desktop app that allocates marketing budgets, extended with an AI feature that recommends campaigns from sales statistics — next to the existing planning logic, not replacing it.
We will tell you in the first call if AI is the wrong tool for the job.
Most of our AI work lands in existing React, Next.js and Node codebases, so the first week is spent reading your system, not selling a rewrite. Whether to build at all is a question we answer honestly in custom software vs off-the-shelf tools.
The same people design the prompt tests, the retrieval pipeline and the fallbacks — nothing gets lost between the notebook and production.
Code, prompts, evaluation sets and provider accounts stay in your repositories and your billing. No proprietary wrapper, no lock-in when the engagement ends.
AI integration is adding AI capabilities — usually an LLM, an agent or retrieval over your data — to software you already run, without rebuilding it. The AI runs as a separate service your product calls through an API, so your database, UI and business logic stay intact. At IvorySoft an integration project starts with a one-week discovery that decides which capability fits (generation, classification, an agent or RAG), then a prototype on your real data behind a feature flag, then a production rollout with monitoring and fallbacks.
AI integration connects existing models — OpenAI, Anthropic, open-source — to your product and data. AI development trains or fine-tunes models. Almost every SaaS and web product needs integration, not development: the model is a commodity, the work is in retrieval, prompts, evaluation, guardrails and cost control. We take on fine-tuning only when an evaluation set proves that prompting and retrieval cannot reach the target quality.
A discovery week, a two-to-three-week prototype on your data, and a rollout phase — typically 4–8 weeks from kickoff to production for a first feature such as an assistant, an extraction pipeline or an agent. Adding a second capability on the same foundation is usually two to three weeks. Timelines stretch when data is scattered or access is unresolved, which is exactly what the discovery week surfaces.
Two numbers matter. The engineering budget starts with a fixed $4,900 discovery week, credited toward the build if we proceed; the prototype and rollout are priced after discovery, so you never sign an open-ended retainer. The second number is the recurring model spend — we design for it from day one with caching, routing simple requests to cheaper models and per-tenant limits, and give you a cost-per-1,000-requests figure before launch, so the API bill is a known number rather than a surprise on the first invoice.
Yes — that is our default stack. We built the Sourcd.ai OpenAI-powered assistant with React, a Node back end on AWS and Auth0, and we integrate through a provider abstraction so a product can switch between OpenAI and Anthropic models without touching its features. Mobile products get the same layer through React Native.
No. The integration runs as a separate service behind your existing API and a feature flag, so current users see nothing until you switch a cohort on. Your database schema and UI stay as they are; the AI service reads through the same permissions your app already enforces. Rollback is turning the flag off. The only changes inside the existing codebase are the calls to the new service and the UI for the feature itself.
Data stays in your cloud account; we call model providers with the minimum context a feature needs, strip identifiers where the feature allows, and use provider settings that exclude your data from training. For regulated products — fintech, healthcare — we add a human review step, prompt and response logging with PII redaction, and per-tenant isolation. You get a one-page data-flow diagram as part of discovery, useful for your own security questionnaires.
