AI AUTOMATION
Turn Repetitive Workflows Into Production AI Systems
G-2 provides AI automation services for B2B companies — from workflow orchestration and LLM integration to RAG, AI agent development, and predictive analytics. The goal is a working system that fits your data, tools, workflows, and operational requirements — not another AI demo.
Most AI Pilots Never Reach Production. We Build the Ones That Do.
A model is only one part of an AI system. Production work also requires integrations, data retrieval, controls, testing, human escalation, logging, and deployment.
We build the engineering layer around the model so the automation can operate inside the systems your business already uses.
Operations and finance teams
repetitive invoice processing, data entry, reporting, or operational handoffs.
CTOs and product teams
AI features or agents that require engineering capacity and integration with existing products.
Compliance and governance teams
AI workflows that need defined controls, logging, and human oversight.
Founders scaling operations
workflows where automation could reduce repetitive manual work without redesigning the whole operation.
WHO IS THIS FOR?
AI Workflow Automation
Connect CRM, ERP, ticketing, email, spreadsheets, and internal tools so data and tasks move between systems with less manual handoff. We use rule-based automation where rules are enough, and AI where the input is too variable for fixed logic.
LLM Integration & RAG Development
Integrate large language models with business data and use Retrieval-Augmented Generation (RAG) to retrieve relevant information instead of passing an entire knowledge base to every request.
AI Agent Development
Build agents that can use tools and functions inside systems such as CRM, ERP, and helpdesk platforms, with defined action boundaries and escalation to a human where required.
Predictive Analytics
Develop models for use cases such as forecasting, scoring, churn prediction, and anomaly detection where the required data and evaluation criteria are available.
What We Automate?
Sales
lead qualification, routing, CRM updates.
Finance
invoice processing, reconciliation, payment reminders.
Customer Support
ticket classification, knowledge retrieval, tier-1 assistance.
Documents
contracts, orders, reports.
Supply Chain
order confirmations, inventory triggers, supplier communication.
Internal Knowledge
RAG over approved internal documentation.
How We Work
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1 — Scope & Prioritise
map workflows and identify realistic automation candidates.
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2 — Design & Build
define architecture, integrations, and the automation itself.
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3 — Validate & Harden
test real cases, edge cases, controls, and human escalation.
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4 — Deploy & Hand Over
production deployment, documentation, and team handover.
What You Get
A production-focused AI system
RAG architecture where relevant
LLM and tool integrations
Human oversight where require
Workflow logging
Documentation
An agreed approach to measuring business impact
NOT SURE WHERE TO START WITH AI AUTOMATION?
Why G-2?
Software engineering first, AI second. G-2 approaches AI automation as a software system that needs to integrate with real products, data, APIs, and business workflows.
Where relevant to the engagement, established guidance such as the NIST AI Risk Management Framework or OWASP guidance can inform system design. Specific framework experience should be confirmed against the project scope before being presented as a G-2 credential.
What Determines the Cost?
Cost depends on architecture, data readiness, integration complexity, automation scope, model usage, and ongoing operational requirements.
What are AI automation services?
The design and development of systems that use AI and workflow automation to perform defined business tasks inside existing tools and processes.
How is AI automation different from RPA?
RPA is typically built around deterministic rules and repeatable interfaces. AI automation can handle less structured inputs and reasoning tasks where fixed rules are not sufficient.
Why not connect directly to an OpenAI or Claude API?
A direct API call is only one component. Production systems also need data handling, integrations, controls, testing, monitoring, and deployment.
How do you use RAG?
RAG retrieves relevant information from approved business data and passes that context to the model for a response or action.
How do you handle AI mistakes?
Controls can include validation, human escalation, restricted actions, testing against edge cases, and decision logging, depending on the use case.
Can you integrate AI with our existing systems?
Yes. AI workflows can be connected to CRM, ERP, helpdesk, document systems, internal knowledge bases, and other APIs within scope.
Start With a Scoping Call
Tell us which workflow you want to automate, which systems are involved, and what the current manual process looks like.