DapkeSocial video app with live streaming and PK battles.
Lika Real EstateProperty discovery platform for listings and leads.
BurpoutSocial fitness challenges that keep people moving.
ChatLivoConnected customer communication SaaS workspace.
EmoTalesAI storytelling adventures designed for children.
EarnifyTrack Income from Multiple Sources with Analytics & Tax Planning.
Your software already has users, data, workflows, and business logic — you shouldn’t have to replace it just to add AI. AI integration for existing software means adding LLMs, RAG, copilots, intelligent search, document processing, and approved AI actions into your current SaaS, web, mobile, or CRM system — modernizing only what’s actually required.
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These aren’t just AI problems. They’re integration, architecture, security, and business logic problems at the same time.
Not every feature belongs in every product — the right ones depend on what your users actually need.
Context-aware assistance inside existing dashboards or internal applications.
Semantic, natural-language search across product data, documents, or approved knowledge.
Ground AI responses in approved company or customer data.
Extract, classify, summarize and organize information from documents.
Draft responses, descriptions, reports or emails inside existing workflows.
Relevant suggestions or ranking, where the available data actually supports them.
AI performs approved actions through your existing APIs and business logic.
Speech, image, or multimodal capabilities where they solve a genuine product problem.
From SaaS platforms to legacy systems, we integrate AI into the software your business depends on every day.
Existing system replaced, data migration, user retraining, large scope, higher disruption. May be justified when the architecture is genuinely unsustainable — but that’s the exception, not the default.
Keep the existing core, add targeted AI capabilities, reuse current data and APIs where appropriate, roll out incrementally, lower disruption. Modernize only the components the AI feature actually requires.
We won’t promise that every legacy application can accept modern AI without changes. During discovery, we identify what can remain untouched, what needs an API or middleware layer, and what genuinely needs modernization.
Illustrative stack: your current backend, PostgreSQL/MySQL, Redis, pgvector/Pinecone/Weaviate/Qdrant, S3-compatible storage, and your existing cloud environment — the AI layer connects to what you already run, not a fresh build.
Web · Mobile · Dashboard
Authentication · Business Logic · Database · APIs
API Gateway · Orchestration · RAG · Model Calls · Tool Calling · Validation · Guardrails
LLMs · Embeddings · Vision · Speech · Specialized Models
CRM · ERP · Documents · Storage · Third-Party APIs
The exact architecture depends on your existing codebase. Possible approaches include direct API integration, a separate AI microservice, middleware, event-driven integration, or extending your existing backend directly — scoped after reviewing what you’ve already built.
No rebuilds, no rip-and-replace — Primocys adds AI directly into your existing software, backed by 1,200+ products delivered and 8+ years of production engineering experience.
Possible sources: your database, product catalog, support articles, PDFs, policies, CRM records, tickets, knowledge base, customer-specific documents, and internal documentation.
A user should not gain access to information through AI that they could not access through the original application.
AI can retrieve an order, draft a response, create a support ticket, schedule an appointment, update a CRM record, prepare a report, classify a request, or trigger an approved workflow — but it should generally call controlled application functions and APIs rather than receive unrestricted access to production systems.
“Move this opportunity to follow-up.” This example shows how a simple user request moves through intent recognition, permission checks, and a controlled API call before a result is validated, returned, and logged.
Existing applications may lack modern APIs, clean service boundaries, current frameworks, or structured documentation. That’s a real constraint, not a reason to assume a rewrite is required.
Possible work: an API wrapper, a middleware layer, a database service, a background worker, an event/webhook layer, selective modernization, or a separate AI service alongside the existing application.
Sometimes the AI is the easy part. The real engineering work is creating a safe connection between a modern model and software that was never designed to expose its data or actions that way.
No single AI provider is right for every feature. We select the model per use case based on quality, latency, cost, and what the feature actually needs to do.
OpenAI, Anthropic, Google Gemini, Azure-hosted AI services, appropriate open-source/open-weight models, or specialized AI APIs.
Quality, latency, cost, privacy, context requirements, multimodal capability, tool calling, and deployment requirements.
No single provider is universally the right choice — we select per feature, and don’t build the integration around a temporary model version number.
A proof of concept only has to answer a few test prompts. Production AI has to handle unexpected input, provider failures, permission boundaries, slow responses, usage spikes, and cases where the model simply shouldn’t answer.
Coverage includes input validation, structured outputs, timeouts and retry logic, rate limits, fallback behavior, RAG evaluation, tool-call validation, human review where required, logging, monitoring, usage/cost tracking, and staged rollout via feature flags where appropriate. We don’t promise zero hallucinations or 99.9% AI accuracy — no one can, honestly.
If your existing product uses a different technology, that doesn’t automatically mean it needs to be replaced. We first evaluate the integration points available in the current architecture.
Whether it’s a ChatGPT/OpenAI prototype, an internal proof of concept, a developer experiment, a standalone chatbot, or a basic RAG prototype — most are missing the same production pieces:
We build around what already works.
The pieces a real product actually needs.
Real user accounts and access control, not a shared prototype login.
Connected to your real systems instead of a sample dataset.
A real interface built for your users, not a developer test screen.
Wired into the workflows and rules your business actually runs on.
Visibility into what the AI actually said and did, not just uptime.
Usage limits and model selection so costs don’t scale unpredictably.
A defined fallback path instead of silent failure when something breaks.
A layer to operate, observe, and support the product after launch.
Not every business needs a new AI product. Sometimes the right move is integrating AI into the software you already have — we help you decide which.
Best when the existing software already works, you have existing users/data/workflows worth keeping, a specific AI capability is needed, and replacement isn’t justified.
Best when building a greenfield product, AI is the core product itself, the existing architecture genuinely can’t support the business direction, or a new standalone experience is required.
If integration is the simpler answer, we’ll tell you. If the existing architecture makes integration more expensive than modernization, we’ll tell you that too.
Adding AI to software that already works means respecting the existing codebase, not replacing it — integration engineering that fits around what your team has already built.
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experience that spans well beyond AI, across production software of real scale, age, and complexity.
integrations scoped and built by engineers who understand the trade-offs, not junior developers wiring an API call.
one team handling the AI layer and every surface it touches, instead of coordinating across separate vendors.
comfortable reading, respecting, and extending code we didn’t originally write, without a rebuild-first bias.
connecting new AI capability to your existing APIs, data, and systems without breaking what already works.
grounded retrieval and model selection handled by engineers who build this regularly, not a one-off experiment.
production deployment across AWS, Azure, and Google Cloud, scoped to fit your existing infrastructure setup.
support that continues past launch, with documentation and code ownership so your team isn’t locked in to us.
Our AI integration process turns an existing codebase into a production-ready AI feature that fits your architecture, respects your data, and ships without a rebuild.
For a new AI feature with no existing prototype, the process is: discovery → feasibility validation → integration plan → production development → evaluation and QA → controlled deployment.
Cost depends on existing code quality, API availability, AI feature complexity, data sources, RAG or agent/tool-calling requirements, UI changes, security requirements, legacy modernization needs, testing, and deployment. We don’t publish arbitrary fixed prices — every integration gets a scope-based estimate after technical discovery.
Have a project in mind? Get straight answers on our AI integration process, architecture choices, timelines, and pricing — then book a free discovery call .
Share your existing application, the AI capability you have in mind, and any constraints around data or systems. We’ll map out an integration path, architecture, and estimate — built around what you already have, not a rebuild.
Discuss My AI Integration →
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