Most AI initiatives fail at the integration stage, not the modelling stage. A well-trained LLM sitting outside your ERP, CRM, or data infrastructure is an expensive experiment. Perimattic's AI integration services close that gap: we embed AI capabilities directly into the systems your teams already use, so the intelligence is where the decisions get made.
Integrating AI with ERPNext, Salesforce, SAP, HubSpot, AWS, Azure, Google Cloud, and Custom Enterprise Stacks
AI integration is the discipline of connecting artificial intelligence capabilities (machine learning models, large language models, AI agents, or generative AI features) into existing business systems so they operate together in production. It is distinct from AI development, which focuses on building the model itself. Integration is about what happens after the model exists: the APIs, data pipelines, security layers, system connectors, and operational architecture that make AI useful inside the tools your organisation runs on.
In practice, that means connecting a predictive model to your ERP so it triggers purchase orders automatically, embedding an LLM into your CRM so sales reps get AI-drafted call summaries, or routing an AI agent into your ticketing system so it resolves tier-one issues without human involvement. The model is the brain. Integration is the nervous system that connects it to everything else.
Perimattic delivers AI integration as a standalone service for companies that already have a model or LLM selected, and as part of our end-to-end AI development and consulting engagements when clients need both. Every engagement is built for production, not proof-of-concept.
Primary focus
Building and training the AI model
Primary focus
Connecting the model to your existing business systems
What it delivers
A trained, validated AI model or LLM
What it delivers
AI capability embedded in your ERP, CRM, and workflows
Core work
Data preparation, model training, fine-tuning, validation
Core work
APIs, data pipelines, connectors, security layers, observability
When you need it
No AI capability exists yet; you need the model built
When you need it
You have a model selected and need it inside your systems
End result
A capable model that exists outside your operational systems
End result
Intelligence where decisions are made, in production
The distinction matters most when you already have an AI model or LLM selected. Most AI failures are integration failures, not model failures. A well-trained model sitting outside your operational systems delivers nothing.
Whether you are embedding a generative AI layer into a customer-facing product, connecting a predictive model to your operational data, or integrating AI agents into enterprise workflows, Perimattic provides the full integration stack. Every engagement is built for production, not proof-of-concept.
Integration of OpenAI GPT-4o, Anthropic Claude, Google Gemini, and open-source LLMs into your existing products and workflows. Includes RAG pipeline setup, API gateway design with rate limiting and fallback routing, prompt management infrastructure, and streaming architecture for low-latency AI features.
AI layer embedded into ERPNext, SAP, and custom ERP platforms for intelligent automation within existing workflows. Covers predictive analytics modules, automated document processing, natural language interfaces for ERP data, and AI-generated reporting and anomaly detection surfaced directly in your ERP dashboard.
AI capabilities embedded into Salesforce, HubSpot, and custom CRM platforms. Includes lead scoring models integrated into your pipeline, AI-generated call summaries and email drafts, churn prediction connected to CRM health scores, and conversation intelligence with sentiment analysis and coaching flags.
Design and build of secure REST and GraphQL APIs that connect AI models to any internal or third-party system. Includes middleware and event-driven architectures (Kafka, SQS, webhooks) for real-time inference, microservices integration patterns, authentication and API security design, and third-party AI service integration.
Design and build of data pipelines that ensure AI models receive clean, consistent, and timely inputs. Covers ETL and ELT pipeline modernisation, vector database setup and management (Pinecone, Weaviate, pgvector), data lake and warehouse integration (Snowflake, BigQuery, Redshift), and real-time streaming pipelines.
Integration of autonomous AI agents into ERP, CRM, HRIS, ticketing, and communication platforms. Includes event-driven agent triggers, human-in-the-loop interfaces embedded in approval workflows, secure tool-use permissions, multi-agent orchestration connected to enterprise data sources, and observability for monitoring agent actions.
Integration of AWS Bedrock, Azure OpenAI Service, and Google Vertex AI into your applications and workflows. Covers cloud-native AI deployment with auto-scaling, MLOps pipeline setup, hybrid and multi-cloud integration for data sovereignty requirements, and inference cost optimisation through caching, batching, and model routing strategies.
A five-phase process from initial assessment through production deployment and ongoing monitoring. Every phase produces a concrete output you own.
We review your current technology stack, the AI capability you want to integrate, your data landscape, and your security and compliance constraints. We identify the right integration pattern, surface any data quality or architectural blockers, and produce a scoped integration plan with timelines and estimated costs before any contract is signed.
Our architects design the full integration architecture: API contracts, data flow diagrams, security model, infrastructure requirements, and observability plan. We produce a technical design document your team can review and challenge. No code is written until architecture is approved.
Our engineers build the integration layer: APIs, data pipelines, connectors, middleware, and any custom adapters required to bridge your AI model to your target systems. We use agile sprints with weekly progress reviews. All code is documented, tested, and written to your engineering standards.
Integration testing covers functional correctness, edge case handling, load performance, and security. We test AI outputs for consistency and quality against defined benchmarks. For regulated industries, we conduct a compliance review covering data handling, audit logging, and access controls before any production deployment.
We deploy to your production environment with CI/CD pipelines and rollback capability. Post-deployment, we monitor integration health, AI output quality, and system performance in real time. Monthly optimisation reviews address any degradation, data drift, or usage patterns that require tuning.
We review your current technology stack, the AI capability you want to integrate, your data landscape, and your security and compliance constraints. We identify the right integration pattern, surface any data quality or architectural blockers, and produce a scoped integration plan with timelines and estimated costs before any contract is signed.
Our architects design the full integration architecture: API contracts, data flow diagrams, security model, infrastructure requirements, and observability plan. We produce a technical design document your team can review and challenge. No code is written until architecture is approved.
Our engineers build the integration layer: APIs, data pipelines, connectors, middleware, and any custom adapters required to bridge your AI model to your target systems. We use agile sprints with weekly progress reviews. All code is documented, tested, and written to your engineering standards.
Integration testing covers functional correctness, edge case handling, load performance, and security. We test AI outputs for consistency and quality against defined benchmarks. For regulated industries, we conduct a compliance review covering data handling, audit logging, and access controls before any production deployment.
We deploy to your production environment with CI/CD pipelines and rollback capability. Post-deployment, we monitor integration health, AI output quality, and system performance in real time. Monthly optimisation reviews address any degradation, data drift, or usage patterns that require tuning.
AI integration looks different in every industry. Select a sector to see how Perimattic approaches the specific systems, compliance requirements, and performance expectations involved.
AI integrated into core banking, trading, risk, and compliance systems with strict data governance and auditability requirements.
AI integrated into core banking, trading, risk, and compliance systems with strict data governance and auditability requirements.
HIPAA-aligned AI integration into EHR systems, diagnostic tools, and patient management platforms.
AI integrated into MES, SCADA, IoT sensor feeds, and production management systems for real-time intelligence.
AI integration across TMS, WMS, and supply chain visibility platforms for real-time operational intelligence.
AI integrated into eCommerce platforms, POS systems, and customer engagement tools for personalisation at scale.
AI integration into policy management, claims, underwriting, and customer systems with full audit trail requirements.
These results come from live production integrations, not benchmarks or projections.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy to Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate production-grade integration from demo-quality builds.
Most AI vendors understand models. Fewer understand ERPNext, Salesforce, SAP, and the data architecture of production enterprise systems. Perimattic's integration practice is built on genuine depth in enterprise systems, including our own ERPNext development practice. The integrations we build are designed with the data structures, API patterns, and operational constraints of your actual systems in mind.
AI integration in regulated industries requires security and compliance decisions to be made at the architecture stage. Perimattic builds with GDPR, HIPAA, SOC 2, and ISO 27001 requirements as design constraints from day one. Audit logging, data residency controls, PII masking, and access management are part of every integration architecture we deliver, not optional add-ons.
AI integration fails in production when vendors have only tested against clean, small datasets. Perimattic runs load testing, adversarial input testing, and real-data validation before any integration goes live. We design fallback logic, circuit breakers, and graceful degradation into every integration architecture so your core systems continue operating even when the AI layer encounters issues.
Perimattic does not hand you an integration specification and expect your team to build it. Our engineers deliver the complete integration: APIs, data pipelines, connectors, infrastructure configuration, testing, and deployment. If you also need the underlying AI model built or the broader AI strategy defined, our consulting and development practices connect to the integration engagement without a handover gap.
“Unlike pure-play AI vendors who hand over a model without integration support, or system integrators with no AI depth, Perimattic bridges both sides of the problem.”
AI integration services connect artificial intelligence capabilities (LLMs, machine learning models, generative AI features, or AI agents) to the existing systems, data platforms, and workflows a business runs on. This includes API development, data pipeline construction, ERP and CRM connectors, cloud infrastructure setup, and the security and observability layers needed to run AI reliably in production. AI integration is what turns a model into a business capability.
AI development is the process of building and training AI models: machine learning systems, large language models, or custom AI applications. AI integration is the process of connecting those models to real business systems so they operate within existing workflows. Many organisations have an AI model or LLM selected but need help embedding it into their ERP, CRM, or data infrastructure. Perimattic provides both services independently and as part of combined engagements.
A focused AI API integration connecting an LLM to an existing application can take four to eight weeks. A fuller enterprise integration covering ERP or CRM systems, data pipeline setup, and observability typically runs eight to sixteen weeks. Complex multi-system integrations for large organisations can extend to six months. Perimattic provides a specific timeline estimate after the free integration assessment, based on your actual stack and requirements.
Yes. ERP integration is one of Perimattic's core strengths. We integrate AI capabilities into ERPNext, SAP, and custom ERP platforms, building predictive analytics, automated document processing, intelligent reporting, and natural language interfaces directly into existing ERP workflows. Our ERPNext development practice provides additional depth for clients running on that platform. The integration is designed to work within your ERP's data structures and user interface, not alongside it.
Yes. Perimattic integrates AI capabilities into Salesforce, HubSpot, and custom CRM platforms. This includes lead scoring models, AI-generated call summaries and email drafts, churn prediction connected to health scores, conversation intelligence, and customer segmentation. We build the integration so AI features surface within the CRM view your teams already use, minimising the change management burden and accelerating adoption.
Perimattic integrates across the major LLM providers (OpenAI GPT-4o, Anthropic Claude, Google Gemini, and open-source models including Meta Llama 3 and Mistral) as well as specialised AI APIs for speech, vision, and domain-specific tasks. For enterprises with data sovereignty requirements, we also support fully self-hosted LLM deployments on your own cloud infrastructure. The model selection recommendation is made during the integration assessment based on your use case, latency requirements, and cost constraints.
Perimattic treats security as an architecture decision, not a feature to add later. Every AI integration we design includes: authentication and authorisation controls aligned with your existing identity systems, PII masking and data minimisation before data reaches the AI model, audit logging of all AI queries and outputs, network isolation for AI components, and data residency controls for regulated environments. For healthcare clients, we align to HIPAA. For financial services clients, we align to GDPR and relevant local regulatory frameworks.
Retrieval-Augmented Generation (RAG) is an integration pattern that connects an LLM to your proprietary data sources (documents, databases, product information, policy libraries) so the model can reason over your specific content at inference time without retraining. This is what allows an AI assistant to accurately answer questions about your internal processes, or a customer support bot to cite your actual product documentation. Perimattic sets up and optimises RAG pipelines as part of our LLM integration service.
Yes. Perimattic provides ongoing integration support and optimisation post-deployment. This covers monitoring integration health and AI output quality, responding to incidents, adapting integrations as your underlying systems change, and running periodic optimisation reviews to improve performance and reduce inference costs. Retainer options are available for clients who want continuous support rather than project-based engagement.
Costs depend on the complexity of the integration, the number of systems involved, the data pipeline requirements, and whether an AI model also needs to be developed or procured. Perimattic's AI development packages start at $1,999 per month. Scoped integration projects typically range from $15,000 to $60,000 for focused single-system integrations, with larger enterprise programmes priced higher. A clear cost estimate is provided after the free integration assessment.
Perimattic offers a free integration assessment to review your current technology stack, identify the right integration architecture for your AI use case, and surface any data or security blockers before a line of code is written.
No generic integration templates. No vendor lock-in. Just an integration built for your specific systems, your data, and the outcomes your business needs.