Adya
Field notes from inside Adya
Long reads from inside the platform - articles, frameworks, and playbooks on agentic AI, governance, and the enterprise stack.
Why we publish Wisdom
Most enterprise-AI writing is written by people who have never shipped enterprise AI. Outlooks. Forecasts. 2026 predictions. Things you could write from a desk by reading other people's think pieces.
Wisdom is the other thing. Every piece in here is a thesis, a framework, or a playbook that came out of building the Adya platform - the wins, the dead-ends, the architectural choices, the regulatory reckonings, and the decisions we wish someone had written down before we made them ourselves.
We don't publish on a schedule. We publish when something is sharp enough to defend, and when the evidence is in. If you only have time for one piece, we'll tell you where to start. If you have time for the whole corpus, it's about 5 hours - and we think the cumulative shape matters more than any single read.
Why we stopped chasing big models
Why we stopped chasing big models - and started building a platform
In August 2024 we fine-tuned our own LLM and beat the best models on cataloging by 21%. By late 2024 we realised distribution - not parameters - was the moat that would actually compound. This is the thesis Adya is built on.
Read featuredThree kinds of pieces
Every piece on Wisdom is one of three things. The format tells you what to expect before you start reading.
Article
Long-form positioning on where the agentic-AI era is going. Theses, arguments, category POVs - written from inside the build.
Framework
Decision tools for buyers. Phases of adoption, build-vs-buy maps, governance scaffolds, comparison matrices - the stuff that lives on a whiteboard before a deal.
Playbook
How-to from inside the platform. The workflows we shipped, the bugs we debugged, the architecture choices - with the trade-offs we actually made in the room.
Why we built Adya the way we did
Long reads on why agentic platforms - not point tools - define the next phase of enterprise software.
The $1 Trillion Wake-Up Call
Anthropic's new model is not the apocalypse. The real story is the quiet, machine-speed erosion of security assumptions most enterprises have never been forced to question.
Read articleFramework: the 4 phases of enterprise AI adoption
A practical sequence from DIY experimentation to platform-centric architecture - with the failure modes and outcomes at every step.
Read articleFrom vibe coding to AI-native enterprises
Why agentic platforms - not point tools - define the next wave of enterprise software.
Read articleThe false binary of build vs buy
How to evaluate platforms vs in-house - and why the build/buy frame is the wrong question to ask.
Read articleWhy Adya is both a platform and ready products
On shipping a horizontal platform AND vertical products - and why the dichotomy disappears.
Read articleHow enterprise AI actually lands
Closed-loop GTM, governance-first adoption, and the operational realities of putting agents into the sales, marketing, and customer stack.
We weren't competing on model quality anymore. We were competing against balance sheets.
MIT found 95% of pilots produce no measurable ROI. The pattern is not a tech problem - it's a governance vacuum.
Why 95% of enterprise AI fails - and what governance has to do with it
MIT found 95% of pilots produce no measurable ROI. The pattern is not a tech problem - it's a governance vacuum.
Read articleThe four phases of enterprise AI adoption
From DIY experimentation to platform-centric architecture - with the failure modes at every step.
Read articleClosed-loop GTM: when agents learn from their own outcomes
How feedback loops between sales, marketing, and customer agents compound into a learning system.
Read articleFor the AI-augmented investment committee
Traceable claims at machine speed, board memos built in days not weeks, and the new analytical bar for capital allocation.
When every assertion in a board memo links back to its source sentence, the analytical bar permanently shifts.
The governance moat: traceable claims at machine speed
When every assertion in a board memo links back to its source sentence, the analytical bar permanently shifts.
Read articleThe AI-augmented investment committee
What changes when board-level memos arrive in days, not weeks - and every claim is traceable to source.
Read articleAI for the trillion-euro CAPEX decisions
Multi-agent TEA, SAF pathway analysis, and why energy transitions fail at the model layer - not the policy layer.
The mandates answer whether to invest in SAF. The question is which pathway, at what scale, in what geography, under which policy scenario - and that question cannot be answered with spreadsheets.
The CAPEX gap in SAF: what your board needs to see
Why trillion-euro SAF investments stall at the board level - and what AI-powered TEA can do about it.
Read articleHow a multi-agent TEA system produces 47 scenarios per project
Inside the orchestration architecture compressing 4-6 weeks of CAPEX analysis into 2-3 days.
Read articleWhy energy transitions fail at the model layer, not the policy layer
Policy is decided. Execution stalls because the analytical infrastructure is built for a different era.
Read articleAI-native hiring, end to end
Sourcing, scoring, qualifying, scheduling - and the bias-aware governance that keeps the whole pipeline defensible.
The companies winning the talent war in 2026 are not the ones with the most advanced AI. They are the ones using AI most intelligently - amplifying human expertise where it matters, automating where it does not.
The 4 Phases of AI-Powered Hiring
From sourcing to scheduling - the operational blueprint compressing the 44-day hiring cycle into days.
Read articleBias-aware AI hiring: a working framework
78% of organizations using AI hiring tools lack proper bias assessment. EU AI Act, NYC Local Law 144 and Colorado SB 24-205 converge in 2026.
Read articleChange Orchestration Platform - Partner Program
How global system integrators monetize agentic AI - partner economics, factory models, multi-tenant scale.
The real margin for global system integrators was never in reselling software licences. It is in the services multiplier around governed delivery.
How GSIs actually make money on agentic AI
Worldwide AI spending hits $2.52T in 2026. The real margin was never in reselling software - it's in the services multiplier around governed delivery.
Read articleFrom SI to AI-native: the partner playbook
88% of enterprises use AI in at least one function. Only 39% see earnings impact. The system integrators that close that gap follow a specific maturity curve.
Read articleMulti-tenant agent orchestration at delivery scale
Three operational planes, an agent factory model, and deterministic governance - the architectural blueprint for deploying governed AI agents across enterprise tenants.
Read articleBFSI-grade collections at scale
Compliance by design, 30+ languages, one platform - across the three continents that regulate AI collections.
The regulatory surface for AI-powered collections now spans three continents and four major frameworks. Most platforms bolt compliance on after the fact. That is the wrong order.
Compliance, by design - across regulators
The regulatory surface for AI-powered collections now spans three continents and four frameworks. Most platforms bolt compliance on after the fact.
Read articleFrom 2 to 30+ languages in 4 weeks
India recognises 22 scheduled languages and 121 census languages. 96.7% of the population speaks a scheduled language. Most collections operations don't.
Read articleOne platform, the whole BFSI stack
Most collections operations run on six vendors duct-taped together: a dialer, a CRM, a route planner, an analytics dashboard, a compliance tool, and a prayer.
Read articleClinical-grade AI in regulated workflows
Clinical AI through IRB review, citation discipline, and multilingual remote care.
The FDA has cleared nearly 1,500 AI-enabled medical devices. But when hospital AI systems enter Institutional Review Board review for clinical deployment, the majority do not pass.
Why most hospital AI fails the IRB review
The FDA has cleared nearly 1,500 AI-enabled medical devices. But when hospital AI systems enter Institutional Review Board review, most don't pass.
Read articleCitation discipline in clinical decision support
If a clinical AI system recommends a treatment but cannot trace it to a validated protocol or published guideline, the recommendation isn't clinically deployable.
Read articleMultilingual remote care: a 12-language deployment
537 million adults live with diabetes worldwide. Most digital health platforms serve them in English. The care gap is linguistic, not clinical.
Read articleVoice AI for the contact centre era
Why voice AI replaces BPOs, and what compliance looks like when the voice on the call is generative.
The $128 billion call center outsourcing industry is built on a labour arbitrage model that voice AI renders structurally obsolete. Augmentation is a waystation.
Why voice AI replaces BPOs - not just augments them
The $128B call center outsourcing industry is built on a labour arbitrage model that voice AI renders structurally obsolete. Augmentation is a waystation.
Read articleThe 4 conversations that break call centers
Most contact center failures cluster around four conversation types that systematically defeat human-staffed operations.
Read articleCompliance recording in the age of generative voice
When the voice on a call is AI-generated, every regulatory assumption about call recording changes. GDPR, PCI DSS, HIPAA, the EU AI Act, and TCPA converge.
Read article