AI Acceleration
Agentic system design and LLM orchestration for high-signal automation that actually works.
A SaaS company was spending 20 hours per week triaging GitHub issues.
Not because they had too many issues. Because their support team had to read code, check logs, cross-reference documentation, and determine whether each issue was a bug, feature request, or configuration error.
They didn't need a chatbot. They needed an autonomous agent that could reason about their codebase.
What AI Acceleration Actually Does
We build agentic systems that perform real work - not just answer questions.
These are autonomous entities that can analyse your private data, interact with your APIs, execute multi-step tasks, and make decisions based on context. They handle repetitive cognitive work that currently requires human judgement.
The difference between this and typical "AI integration" is execution capability. These agents don't just suggest actions. They take them.
How It Works
The architecture combines Gemini 1.5 Pro for high-throughput processing with Claude 3.5 for complex reasoning and code analysis.
Agent capabilities:
- Contextual understanding - Access to your codebase, documentation, and business logic
- Multi-step task execution - Break complex workflows into discrete actions
- API integration - Direct interaction with your systems (GitHub, databases, internal tools)
- Verification loops - Self-check outputs before committing changes
- Human escalation - Route edge cases or high-risk decisions to human review
We use Model Context Protocol (MCP) for tool integration and custom orchestration logic to coordinate between multiple LLMs and data sources.
The system runs in your cloud tenant, with fine-grained access controls and audit logging for every action taken.
Real-World Results
A B2B software company was drowning in GitHub issue triage. Their support team spent half their time just categorising and routing tickets.
We built an autonomous agent that:
- Reads incoming GitHub issues and pull requests
- Analyses referenced code and stack traces
- Searches documentation and previous issues for similar patterns
- Categorises issues (bug/feature/support) with confidence scores
- Auto-responds to common config errors with solutions
- Routes complex issues to the right engineering team
- Adds relevant labels and milestone assignments
The outcome:
- 78% of issues now auto-categorised and routed
- Response time dropped from 24 hours to under 2 hours
- Support team reallocated to high-value customer work
- Customer satisfaction scores increased 23%
The agent handles the cognitive grunt work. Humans focus on complex problem-solving and relationship management.
What Makes This Different
These agents actually work
Most "AI automation" is either a chatbot wrapper or brittle workflow automation. We build agents with real reasoning capability and execution authority.
Deep integration with your systems
The agent understands your codebase structure, your business rules, and your data schemas. It's not a generic tool - it's purpose-built for your operations.
Safe by default
Every action includes confidence scoring, verification steps, and human override capability. High-risk operations always route to human approval.
You own everything
All code, configuration, and orchestration logic belongs to you. No vendor lock-in. No ongoing platform fees. Deploy in your cloud tenant with full control.
Common Use Cases
Autonomous support agent Resolve technical issues by analysing codebases, logs, and documentation. Auto-respond to common problems.
Code review automation Analyse pull requests for security issues, performance problems, and style violations before human review.
Data validation and correction Identify and fix data quality issues across databases using business rules and historical patterns.
Intelligent routing and triage Automatically categorise and route customer requests, support tickets, or sales leads based on content analysis.
Research and synthesis Aggregate information from multiple sources, synthesise insights, and generate structured reports.
Technical Stack
- Gemini 1.5 Pro - High-throughput processing and data analysis
- Claude 3.5 - Complex reasoning and code understanding
- MCP (Model Context Protocol) - Standardised tool integration
- Python/Node - Custom orchestration and business logic
What You Get
A production-ready agentic system integrated into your existing workflows.
The agent operates autonomously within defined boundaries. You configure what actions it can take, what data it can access, and when it must escalate to humans.
All agent actions are logged with full audit trails. You can review decisions, trace reasoning, and refine behaviour over time.
The system includes monitoring dashboards showing task completion rates, confidence distributions, and escalation patterns.
Getting Started
AI acceleration works best when there's repetitive cognitive work that follows learnable patterns.
If your team spends more than 15 hours per week on tasks like triage, categorisation, research, or validation, you're likely spending $50k-80k annually on work that can be automated.
Schedule a discovery call to discuss your specific use case. We'll map your current workflows, identify automation opportunities, and build a proof-of-concept agent to validate feasibility before full development.