Bengaluru, Aug 21: Avaali Solutions today announced a major expansion of Avagama, its enterprise AI platform, transforming it from an AI use-case discovery and prioritisation tool into a complete Enterprise AI Lifecycle Platform – the only platform on the market that manages every phase of an enterprise AI programme in one integrated system, from opportunity identification to governed production deployment.
The announcement marks a significant evolution from Avagama’s original positioning as a discovery layer. The platform now covers an eight-step lifecycle – Discover, Evaluate and Prioritize, Recommend AI Approach, Recommend the Right LLM or SLM, Build Agent, Execute, Govern, and Measure.
“When we launched Avagama, we said the enterprise AI battle would be won not by who had the best model story, but by who could identify the right use cases with enough business clarity and discipline,” said Srividya Kannan, Founder and CEO, Avaali Solutions. “We have spent the last several months proving that thesis in production. Today’s expansion is the answer to every enterprise that asked: what comes after discovery?”
From One Step to Eight: What Has Changed
The original Avagama addressed a problem most enterprise AI vendors were ignoring: how to decide which processes deserve AI, in what order, and with what kind of intervention. That capability – structured use-case discovery and prioritisation – remains at the platform’s core.
What is new is everything that follows. Avagama now takes an enterprise from a prioritised opportunity list all the way through to a governed agent running in production – without leaving the platform, stitching together external tools, or starting a separate professional services engagement to activate governance.
The eight-step chain works as follows:
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Discover – Maps the process landscape and identifies AI-ready opportunities based on data quality, integration feasibility, and expected ROI.
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Evaluate and Prioritize – Scores each opportunity across feasibility, strategic importance, cycle-time impact, and risk. Outputs a ranked priority list leadership can act on.
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Recommend AI Approach – Determines whether each process calls for automation, augmentation, or a fully agentic intervention – preventing the most common enterprise AI mistake.
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Recommend Right LLM or SLM – Analyses the accuracy requirements, input complexity, latency tolerance, and cost constraints of each process and recommends the optimal language model. No other enterprise AI platform does this at the process level.
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Build Agent – Configures a production-grade agent and also provides a library of 31 pre-built blueprints across 17 industries.
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Execute – Deploys and manages agents in production with real-time monitoring, confidence threshold controls, and automatic escalation routing.
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Govern – Ships governance capabilities pre-configured: role-based access control, tamper-evident audit trails, human-in-the-loop approval workflows, token cost dashboards, and compliance reporting. Built in from day one.
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Measure – Tracks ROI, cycle-time improvement, and business outcomes per agent, feeding results back into the discovery phase for continuous optimisation.
The Capability No Other Platform Has
The single most significant addition to Avagama is its LLM and SLM Recommendation Engine. When enterprises deploy AI at scale, the instinct is to default to one flagship foundation model across the entire business. The result is predictable: overspend on processes that do not need a large model, and under-performance on processes where a large model is essential.
Avagama now analyses each prioritised process individually – evaluating document complexity, query volume, latency sensitivity, regulatory risk, and cost tolerance – and recommends whether that process should be handled by a frontier LLM, a lighter small language model, or a combination of both. This is not a configuration option for developers. It is a business-level recommendation, with rationale, built into the platform.
31 Blueprints. 17 Industries. Deployed in Hours.
One of the central criticisms of enterprise AI platforms has been the gap between what is demonstrated and how long it actually takes to reach production. Avagama’s blueprint library is its answer to that gap.
The library now contains 31 pre-built, production-tested agent blueprints across Finance, Procurement, Supply Chain, Human Resources, Sales, and twelve other enterprise functions – spanning 17 industry verticals including Manufacturing, Retail, Banking and Financial Services, Energy and Utilities, Healthcare, Pharmaceuticals, Real Estate, Logistics, Automotive, and FMCG.
Each blueprint encodes the process logic, model configuration, integration connectors, and governance controls developed from real enterprise deployments. An organisation selects a blueprint, configures it to its environment, and has a production-grade agent running at a fraction of the time – without months of custom AI development.
Governance That Ships With Every Agent
In a market where AI governance is rapidly shifting from a best practice to a regulatory requirement, Avagama’s governance architecture is a structural differentiator.
Rather than treating governance as a separate module or a professional services engagement, Avagama embeds 17 or more governance capabilities in every agent deployment from day one. These include role-based access control with full role segregation, tamper-evident audit logging, configurable human-in-the-loop approval gates, real-time token cost dashboards, model drift monitoring, and anomaly alerting.