AI Legacy
Integration.

Enterprise AI Integration
Built on GPT-4o, Claude, and Azure OpenAI — embedded directly into the ERPs, CRMs, and enterprise platforms your team already runs.
Built for CTOs, IT directors, and operations teams running enterprise platforms that are too critical to replace but too limited without AI — and organizations that need intelligence inside their existing software, not another tool to manage.
The Third Path

Why Retrofit AI Into Existing Systems.

Most businesses think they have two options: replace everything or accept the limitations. There is a third path. Add AI capabilities directly into the software you already run — no migration, no retraining, no downtime.

01

4-8 Weeks

Discovery to deployment

02

90%

Of systems have a viable integration path

03

60-90 Days

Measurable ROI timeline

04

0

Production disruption during development

Integration Methods

Four Paths Into Your Existing Software.

Every system has integration points. We identify the right approach based on what your software exposes — API endpoints, database access, event hooks, or all three.

REST API Integration

Standard approach for most modern and semi-modern systems. We connect AI processing to existing API endpoints, adding intelligence to every request and response.

  • Connects to documented API endpoints
  • Bi-directional data flow with AI processing
  • Supports authentication, rate limiting, and error handling
  • Ideal for systems with REST or GraphQL APIs
Most common method

Webhook Configuration

System pushes data to an AI processing layer on events — no polling, no delays. Ideal for real-time intelligence like ticket categorization and invoice processing.

  • Event-driven, zero-latency processing
  • Triggered by system actions automatically
  • No constant polling or scheduled jobs
  • Ideal for real-time classification and routing
Real-time processing

Database Direct

Connect directly to the database layer for read/write access. Common with custom-built legacy applications that predate modern API standards.

  • Secure read/write access to existing tables
  • Works with MySQL, PostgreSQL, MSSQL, Oracle
  • No API dependency — works on any database-backed system
  • Ideal for systems 10+ years old without APIs
Deepest access level

Custom Middleware

A custom bridge handling data transformation, authentication, error recovery, and routing between your system and the AI layer.

  • Handles authentication and data mapping
  • Manages rate limits, retries, and error recovery
  • Coordinates multiple integration methods
  • Ideal for complex multi-system environments
Maximum flexibility
Integrations We Build

Real-world Examples of AI Inside Legacy Systems.

Every integration starts with a specific business problem. Here are six integrations we commonly build — each one embeds AI directly into the software your team already uses.

CRM Integration

Ai-powered Lead Scoring in Salesforce

Embedded a scoring model directly into a legacy Salesforce instance. Every new lead is automatically evaluated against historical win patterns, enriched with firmographic data, and routed to the right rep — all inside the existing CRM interface.

Reduces lead review time from 2 hours to 10 minutes daily

ERP Integration

Invoice Processing in NetSuite

Connected an AI document processing layer to a NetSuite ERP via API. Incoming invoices are automatically read, line items extracted, matched against purchase orders, and flagged for exceptions — eliminating manual data entry entirely.

Cuts invoice processing time by 70% with 98% accuracy

Helpdesk Integration

Ticket Triage in Zendesk

Added an AI categorization layer to an existing Zendesk instance via webhook. Every incoming ticket is read, classified by urgency and topic, assigned a priority tag, and routed to the correct support tier — before any human touches it.

Reduces average first-response time from 4 hours to 25 minutes

Custom Application

Semantic Search in Legacy PHP Platform

Built a vector search layer on top of a 12-year-old custom PHP application with 500,000+ records. Users type natural language queries and get instant, relevant results — replacing the old keyword-only search that missed 60% of matching records.

Improves search accuracy from 40% to 95% across all records

HR System Integration

Policy Q&a in BambooHR

Embedded an AI assistant directly into a BambooHR instance via database-direct integration. Employees ask questions about PTO policies, benefits, and onboarding procedures in natural language and get instant, accurate answers sourced from the company handbook.

Handles 80% of routine HR queries without human involvement

Project Management Integration

Auto-summarization in Jira

Connected an AI summarization layer to a Jira instance via REST API. Every completed sprint automatically generates a structured summary with completed items, blockers, velocity trends, and recommendations — delivered to Slack and email on schedule.

Eliminates 3 hours of weekly manual sprint reporting
AI Capabilities

What AI Adds to Your Existing Software.

These are the capabilities we embed directly into the systems your team already uses. Not separate tools. Not external dashboards. Intelligence built into the software itself.

Ai-powered Search

Natural language queries across all records and data. Your team types a question and gets instant results — even across hundreds of thousands of records.

  • Natural language instead of keyword search
  • Vector-based semantic matching
  • Cross-record and cross-table queries
  • Typical result: 50-80% faster data retrieval

Document Processing

Auto-extract, classify, and organize documents and attachments. Invoices, contracts, and support tickets are read, categorized, and routed without manual sorting.

  • PDF, image, and scanned document extraction
  • Automated classification and routing
  • Structured data output to your database
  • Typical result: 40-70% reduction in manual processing

Intelligent Categorization

Auto-tag, classify, and route data based on content. Support tickets get priority tags. Leads get scored. Documents get filed — reducing manual triage significantly.

  • Content-based tagging and classification
  • Priority scoring and escalation routing
  • Custom taxonomy and category mapping
  • Typical result: 40-70% reduction in triage time

Auto-response Generation

Generate drafts, summaries, and responses trained on your historical data. Support agents get suggested replies. Sales teams get personalized follow-ups.

  • Context-aware draft generation
  • Trained on your historical communications
  • Human-in-the-loop approval workflow
  • Typical result: 60% faster response drafting

Predictive Analysis

Forecast outcomes, detect patterns, and surface anomalies from your existing data. Turn historical records into forward-looking intelligence without building a separate analytics platform.

  • Demand forecasting and trend detection
  • Anomaly and outlier identification
  • Pattern recognition across datasets
  • Typical result: 30-50% improvement in forecast accuracy
Comparison

AI Integration vs Full System Replacement.

For most businesses, adding AI to existing software delivers faster results at a fraction of the cost and risk of a full platform migration.

AI Integration (Our Approach) Full System Replacement
Timeline 4-8 weeks 6-18 months
Disruption Minimal — same tools, new capabilities High — new tools, new workflows, learning curve
Data Risk Low — data stays where it is Significant — full migration required
Team Impact No retraining — AI works inside existing interface Full retraining required across all teams
ROI Timeline 60-90 days to measurable ROI 12-24 months to recoup investment
Best When System works but needs AI-powered intelligence System is end-of-life or fundamentally broken
Technology Stack

Models and Tools We Integrate With.

We select the AI model based on your task requirements, cost considerations, and data sensitivity. The integration technology is chosen based on what your system exposes.

OpenAI Gpt-4o

Complex reasoning, structured output, and function calling. The default for integrations that require nuanced language processing, categorization, and multi-step decision logic.

Anthropic Claude

Long-document processing, careful instruction following, and extended context windows. Ideal for integrations involving legal documents, knowledge bases, and detailed technical content.

Azure OpenAI

Enterprise-grade deployment with data residency controls, SOC 2 compliance, and no training on customer data. The choice for regulated industries and sensitive data requirements.

Pinecone and Pgvector

Vector databases that power semantic search inside legacy systems. Store embeddings alongside your existing data for natural language queries across millions of records.

LangChain and LangGraph

Orchestration frameworks for building multi-step AI processing pipelines. Manage state, handle retries, and coordinate complex workflows between your system and the AI layer.

Custom Middleware

When no standard integration path exists, we build custom middleware in Node.js or Python that handles authentication, data transformation, rate limiting, and error recovery.

Compatible Systems

Systems We Integrate AI Into.

We have deployed AI integrations across CRMs, ERPs, helpdesks, project management tools, HR systems, and custom-built applications. If your system has an API, database access, or event hooks — it qualifies.

CRM Platforms Salesforce, HubSpot, Pipedrive, Zoho, Microsoft Dynamics, and custom CRMs — AI features: natural language search, lead scoring, auto-response drafting
ERP Systems SAP, Oracle, NetSuite, Microsoft Dynamics 365, and custom ERPs — AI features: invoice processing, demand forecasting, anomaly detection
Support and Helpdesk Zendesk, Freshdesk, ServiceNow, ConnectWise, and custom ticketing systems — AI features: ticket categorization, response suggestions, escalation routing
Project Management Jira, Monday.com, Asana, and custom PM tools — AI features: task prioritization, status summarization, resource allocation recommendations
HR and HRMS Workday, BambooHR, ADP, and custom HR systems — AI features: resume screening, policy Q&A, onboarding automation
Custom Applications PHP/Laravel, Python/Django, Node.js, Legacy .NET — any system with API or database access, AI capabilities adapted to your architecture
How It Works

From Discovery to Deployment In 4-8 Weeks.

Every integration follows the same structured process. No shortcuts on testing. No production surprises. You provide access — we handle everything else.

1

Discovery and Feasibility

Review API documentation, system architecture, and database structure. You receive a detailed report with recommendations and a fixed-price quote before development begins. 3-5 business days.

2

Environment Setup and Development

Configure staging access, establish API connections, and develop custom integration code against real data with AI functionality implemented. 2-4 weeks.

3

Testing and Refinement

End-to-end testing with real data volumes. Performance optimization, security review, and team validation. Edge cases and failure recovery tested against live scenarios. 1-2 weeks.

4

Deployment and Handover

Coordinated production deployment with rollback procedures. Full technical documentation covering architecture, API endpoints, and maintenance. Your team owns it from day one. 2-3 business days.

Requirements

What You Provide. What We Handle.

AI integration requires partnership. You provide access and context. We handle architecture, development, testing, and deployment.

01

System Access

API documentation or database access credentials. Read access to staging or development environments is required.

  • API docs or database schema
  • Staging environment credentials
  • Authentication method details
02

Staging Environment

A non-production instance of your system for safe experimentation. We never touch production until integration is tested end-to-end.

  • Dev or staging instance access
  • Sample data for testing
  • Deployment pipeline access
03

Stakeholder Availability

2-3 review sessions during the project. Total time commitment is typically 4-6 hours across the full project timeline.

  • Technical point of contact
  • Business stakeholder for validation
  • Feedback within 48 hours at milestones
04

Platform Costs

AI model usage fees billed directly to your account. We help you set up the right plan and estimate monthly costs during discovery.

  • OpenAI, Azure, or Anthropic account
  • Usage-based API billing
  • We estimate costs during discovery

In Practice

Old Systems. New Intelligence.

Legacy systems do not need to be replaced to become intelligent. Here is how three businesses added AI without rebuilding.

Distribution — ERP Order Prediction

The problem: A wholesale distributor ran a 15-year-old ERP system that tracked orders, inventory, and shipping. The system worked but had no predictive capability — stockouts happened monthly because reorder decisions were based on gut feel. Replacing the ERP was a $2M project nobody wanted to fund.

What we built: AI middleware that read historical order data from the ERP via database queries, applied time-series forecasting models, and wrote reorder recommendations back into the ERP as pending purchase orders. The existing system stayed untouched — the AI layer sat alongside it.

The outcome: Stockouts decreased 68% in the first quarter. Overstock carrying costs dropped 22%. Purchasing managers reviewed AI-generated reorder suggestions instead of building them manually. The ERP continued running exactly as it had for 15 years — with an intelligence layer the vendor never built.

Healthcare — Claims Document Processing

The problem: A medical billing company processed claims through a legacy system that required manual data entry from scanned documents — explanation of benefits (EOB) forms, referral letters, and insurance cards. Data entry staff processed 150 documents per day with a 6% error rate.

What we built: Document AI pipeline that scanned incoming PDFs, extracted structured data using OCR and entity recognition, validated against payer databases, and pushed cleaned data directly into the legacy billing system through its existing import interface. No API modifications required.

The outcome: Processing capacity tripled from 150 to 450 documents per day without adding staff. Error rate dropped from 6% to 0.8%. Staff shifted from data entry to exception handling and payer follow-ups. The legacy billing system required zero modifications.

Real Estate — CRM Intelligence Layer

The problem: A commercial real estate firm used a CRM that stored 8 years of deal data, tenant information, and property records — but offered no analytical capabilities beyond basic reporting. Brokers could not quickly identify which tenants were likely to renew, expand, or leave.

What we built: AI analytics layer that connected to the CRM database, applied predictive models to tenant behavior (lease renewal probability, expansion likelihood, churn risk), and surfaced insights through a dashboard. The CRM data stayed in place — the AI read from it and displayed results in a separate interface.

The outcome: Brokers identified at-risk tenants 90 days earlier than before. Renewal conversations started proactively instead of reactively. Tenant retention improved 14% in the first year. The firm’s existing CRM — with all its customizations and 8 years of data — continued operating unchanged.

Tell Us Which System Needs to Get Smarter.

Describe your system and the AI capabilities you need. We assess feasibility and show you what is possible — typically within 48 hours.

Most integrations are live in 4-8 weeks.

FAQ.

We can work directly with the database or build custom middleware. Most systems — even those built 10-15 years ago — have at least one viable integration path. During discovery, we audit your system architecture and identify the best approach.
No. All development happens in a staging or development environment first. We never touch production until the integration has been tested end-to-end with real data. Rollback procedures are documented before go-live.
Not always. If the system has documented API endpoints, we can integrate without touching the codebase. For database-direct or middleware integrations, we may need read access to the schema.
Workflow automation connects tools through no-code platforms like Zapier or Make. Legacy integration involves custom development at the API or database level, embedding AI capabilities directly inside the system your team already uses. The result is deeper functionality and no external tool dependency.
4-8 weeks from discovery to deployment. Simpler integrations can be live in 4 weeks. Complex integrations involving multiple systems or database-direct access typically take 6-8 weeks.
Yes. As long as we have API documentation or database access, we can integrate with any system regardless of who built it. We have integrated AI into custom PHP applications, legacy .NET systems, Python-based platforms, and enterprise tools.
We use enterprise-grade AI platforms like Azure OpenAI that offer data residency controls, SOC 2 compliance, and no training on customer data. Data handling protocols are scoped during discovery and documented before development begins.
You receive full documentation covering architecture, API endpoints, error handling, and maintenance procedures. Your team owns the integration entirely. We also offer maintenance retainers for ongoing support.
Every project starts with a discovery phase where we evaluate your system architecture, integration points, and AI capabilities required. Based on this evaluation, we provide a fixed-price quote before any development begins. There are no surprises — you approve the scope and cost before we write any code.
Most clients see measurable ROI within 60-90 days. Common outcomes include 40-70% reduction in manual processing time, 50-80% faster data retrieval, and significant reductions in human error rates.
A custom AI agent is a standalone application with its own interface. AI legacy integration embeds intelligence directly into software your team already uses, without adding a new tool. Both use the same underlying AI models, but the delivery method differs.