AI Model & Integrations
Making AI work inside real systems, not just in isolation. Most AI initiatives don't fail because of the model. They struggle because models are hard to integrate, operate, and evolve inside real products. We help teams integrate AI models and platforms into production systems in a way that is secure, observable, maintainable, and aligned with how the business actually runs.
The challenge teams underestimate
Connecting an AI model to a real system is not a plug-and-play task.
These are integration problems, not AI problems.
Models work in notebooks but break in production
What looks impressive in isolation often fails under real usage patterns and edge cases.
Vendor lock-in limits flexibility
Switching models or providers later becomes expensive and risky without abstraction.
No visibility into failures or costs
Teams struggle to debug issues or predict expenses as usage scales.
What AI integration really involves
Effective AI integration means treating models as operational components, not experiments.
Systems that can switch models without rewrites
Visibility into model behavior, costs, and failures
Security, compliance, and governance built in
Integrations teams can own and operate independently
How we approach model & platform integrations
We focus on integration that survives real usage. The goal is confidence, not novelty.
AI systems that can be operated, not babysat
Clear understanding of model behavior and limits.
Reduced long-term risk
Flexibility to evolve as models and vendors change.
Security and governance built in
Data handling, access control, and auditability from the start.
Confidence under pressure
AI features won't quietly fail when it matters most.
What we integrate with
We work across a wide range of AI models and platforms:
Large language models
OpenAI, Anthropic, open-source models, and custom fine-tuned models.
AI & ML platforms
Cloud AI platforms (AWS, Azure, GCP), managed inference, and training services.
Vector databases and retrieval
Pinecone, Weaviate, Qdrant, pgvector, and hybrid search systems.
Product and enterprise systems
Web and mobile apps, internal tools, data platforms, and existing enterprise software.
Common patterns we fix
Models work in demos but break in production
Vendor lock-in limits future options
Costs grow faster than expected
Teams fear touching the integration layer
Sometimes we integrate new models. Sometimes we stabilize what already exists. Both are wins.
Technology Stack
We work across the entire AI ecosystem. No vendor partnerships, no preferences
Model Providers
OpenAI
Anthropic
Open-source modelsIntegration & Orchestration
Custom abstraction layers
API gateways
Vector databasesInfrastructure
AWS / Azure / GCP
Secure deploymentsTechnology serves the integration, not the other way around.
What Working With Chromosis Feels Like
You won't get:
Our goal is systems you can own and operate.
You will get:
Model-agnostic design
Switch providers without rewriting your product.
Operational clarity
Teams understand what's happening and why.
Long-term flexibility
Integrations evolve as platforms and requirements change.
Common Questions
Can you work with our existing AI setup?
Yes. We often integrate into or improve existing systems rather than replacing everything.
Are you tied to specific AI vendors?
No. We design vendor-agnostic integrations wherever possible.
Do you help decide which models or platforms to use?
Yes. Selection is part of the integration strategy, not a separate decision.
How do you handle failures or low confidence outputs?
We design fallback paths, confidence thresholds, and escalation mechanisms.
What does handoff look like?
Clear documentation, ownership clarity, and systems your team can run independently.
Let's talk about your AI integration
If you're moving AI from experimentation into real systems, the integration layer matters more than the model itself. Let's discuss how to make it reliable, understandable, and future-proof.
No hype. Just practical engineering decisions.