
Agentic Workflows
Elevate response accuracy to 80%+ and reduce costs by ~40%. Our Agentic Workflows utilize a coordinated approach, assigning specific tasks to agents under a supervisory model for high-stakes tasks.
Agentic AI is software that plans and completes goals autonomously, going beyond assistants that only respond to prompts. Affine builds production agentic AI that runs multi-step enterprise workflows - cutting costs by ~40% and lifting accuracy to 80%+.
Most enterprises have the data. Most have the models. What they lack is the production infrastructure to make AI act- autonomously, accurately, at scale. Affine's Agentic AI closes that gap.
Each layer is independently deployable and proven in production - combine them for compounding returns.

Elevate response accuracy to 80%+ and reduce costs by ~40%. Our Agentic Workflows utilize a coordinated approach, assigning specific tasks to agents under a supervisory model for high-stakes tasks.

Generate accurate answers from extensive datasets. By integrating Knowledge Graphs, we provide richer contexts, enhance multi-hop reasoning, and anchor responses in verified data.

Cut operational costs by ~40% with Semantic Caching. By utilising user feedback, we refine prompts and models, improving latency and ensuring that user interactions are swift and relevant.

Dramatically reduce manual effort by 60–70% and cut costs by ~40% with our AutoEvaluation technology. This tool autonomously assesses AI outputs, ensuring consistent and reliable performance at scale.
The proprietary systems that power the four capabilities above - each battle-tested in enterprise deployments, working in concert.
Autonomously assesses AI outputs with consistent, reliable performance metrics - eliminating manual QA bottlenecks.
Transforms unstructured data - emails, invoices, PDFs - into structured intelligence your systems can act on instantly.
Customise open-source LLMs to your domain while keeping data private. Tuned models deliver more relevant, secure outputs.
Reduce manual data creation by 50–60% and cut costs by ~40%. Synthetic data accelerates AI system maturation at scale.
Production AI-agent framework automating development, testing, deployment, and support workflows with minimal human intervention.
Self-learning conversational agents resolving customer queries across channels with consistent brand voice and instant escalation.
Agentic infrastructure monitoring framework predicting disruptions, rerouting load, and self-healing across multi-cloud estates.