LLM API integration
Production-grade connections to OpenAI, Anthropic, Google and open-source model endpoints, with retries, streaming, structured outputs and prompt versioning handled in code.
AI Services
AI integration services that fit your stack.
Our AI integration services bring language models, predictive models and AI APIs into the software you already run. We handle the plumbing, authentication, data mapping and error handling so AI becomes a dependable part of your workflows instead of a separate tool.
- No rip-and-replace
- API-first architecture
- Secure data flows
01 Overview
Make AI work inside the tools you already use
Adoption suffers when AI lives in a separate tab. Teams get value when suggestions appear inside the CRM record, the support ticket or the warehouse dashboard they open every day. Our AI integration services connect models to those surfaces through APIs, webhooks, event streams and plugins, with permissions inherited from the systems themselves.
Integrating AI into existing systems raises practical questions: which data can leave your network, how to handle provider outages, how to keep latency acceptable and how to control spend. We design for each of these with gateways, caching, fallback models, redaction and usage limits, so LLM API integration stays reliable as traffic grows.
02 Capabilities
AI Integration capabilities.
End-to-end AI Integration: strategy, engineering and the production operations that keep it working.
CRM and ERP connectors
Integrations with Salesforce, HubSpot, SAP, Dynamics and similar platforms so AI can read context and write results back without manual copy and paste.
AI workflow integration
Event-driven pipelines that trigger models when a document arrives, a ticket opens or a record changes, then route results to the next step automatically.
Model gateway and routing
A central layer that manages keys, routes requests between models by cost or capability, enforces quotas and records every call for audit and debugging.
Data privacy controls
PII redaction, regional endpoints and private deployments that keep sensitive fields out of third-party models, designed around GDPR and HIPAA requirements.
Legacy system modernization
Wrapping older databases and on-premise applications with clean APIs so they can feed and consume AI services without a risky full rewrite.
03 How we deliver
From first call to production.
Every AI Integration engagement follows the same five stages, so you see value early and risk stays low.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
04 Use cases
Where it pays off.
Practical, high-value applications we design and build for clients across industries.
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UC-01
AI summaries inside the CRM
Sales reps see call summaries, next-step suggestions and account notes generated directly on the record, drawn from emails, meetings and past activity.
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UC-02
Automated ticket triage in helpdesk tools
Incoming tickets in Zendesk, Jira or ServiceNow get categorized, prioritized and matched with pre-drafted replies and relevant knowledge articles before an agent ever opens them.
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UC-03
Document intake for ERP workflows
Purchase orders and invoices are parsed, validated against master data and posted to the ERP, with exceptions sent to a review queue.
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UC-04
Embedding AI in a mobile app
Enterprise AI integration for mobile products through a secure backend proxy, adding chat, image understanding or recommendations without exposing API keys on devices.
05 Technology
The stack we use.
Proven, well-supported tools chosen per project for accuracy, cost and long-term maintainability.
AI providers
- OpenAI API
- Anthropic Claude API
- Google Vertex AI
- Azure OpenAI Service
- AWS Bedrock
Integration layer
- REST and GraphQL
- Apache Kafka
- n8n
- Zapier
- LiteLLM
Business platforms
- Salesforce
- HubSpot
- SAP
- Microsoft Dynamics 365
- ServiceNow
06 Industries
Built for your sector.
We deliver AI Integration for regulated and fast-moving industries alike.
What are AI integration services?
AI integration services connect AI models and APIs to the software a business already uses, such as CRMs, ERPs, helpdesks, databases and custom apps. The work covers data mapping, authentication, workflow triggers, error handling, monitoring and security controls, so AI outputs flow into existing processes reliably.
Can you integrate AI into existing systems without replacing them?
Yes, that is the usual approach. We add AI through APIs, middleware, plugins or event streams around your current applications. For older systems without modern interfaces, we build a thin API layer first. Replacing core platforms is rarely necessary to get value from AI.
How do you handle OpenAI API integration securely?
API keys stay on the server side, never in client apps. Requests pass through a gateway that applies authentication, rate limits and logging. Sensitive fields can be redacted before leaving your network, and we use enterprise endpoints or regional deployments when data residency or retention terms matter.
What happens if an AI provider goes down or changes its model?
We design for that from the start. A routing layer can fail over to a secondary model, and non-urgent tasks can be queued for retry. Prompts and model versions are pinned and tested, so provider updates are evaluated against your test set before they reach users.
How do you keep AI integration costs under control?
Cost controls include caching repeated requests, routing simple tasks to smaller models, trimming context sent with each call, batching where latency allows and setting per-team usage quotas. Dashboards show spend by feature, so you can see which integrations deliver value relative to what they cost.