Cordiant
The dialog is AI. The execution is application code.
Reliable, deterministic, brand-consistent conversational applications delivered on a proprietary three-layer platform. Five months to first production. $320,000 per engagement.
Fifty to two hundred employee-facing systems. One workforce that has to move between all of them.
HRIS, ITSM, ERP, expense, procurement, travel, CRM, LMS, facilities, plus the long tail of homegrown and SaaS tools that accumulated around them. Each with its own UI, its own navigation, its own terminology, its own training burden, its own release cadence. Your employees move between them all day.
This is not a software problem. It is a surface problem. The employee-facing layer is bound one-to-one to each system of record. Decouple the surface from the system, and the problem collapses.
Every system is a curriculum. New hires spend ninety days learning tools instead of doing work. Tenured employees pay a recurring tax every time they touch a system they use rarely. L&D budgets swell year after year not because skills changed, but because interfaces did.
Every vendor UI redesign is a full-enterprise event. Every migration — Workday to Oracle, Concur to Navan, ServiceNow to Jira — triggers a training deck, a comms plan, a help-desk surge. Some migrations that would deliver better economics get shelved because the change cost exceeds the savings.
The quietest and largest cost. Planning a customer trip takes five actions across four tools. Each switch is a login, a re-learning, a context load. Multiplied across thousands of flows per employee per year, the aggregate productivity loss dwarfs the other two costs combined.
The mainstream enterprise AI conversation is about agents — autonomous systems reasoning, deciding, acting. The pitch is compelling in a demo and terrifying in a production environment governed by SOX, HIPAA, GDPR, PCI, a board audit committee, or any compliance regime that requires a human to be named for every action that touches a system of record.
Cordiant takes the opposite position. We build conversational applications. The dialog is intelligent; the execution is deterministic. An employee speaks or types; our platform interprets the intent; but the action that follows runs as application code against your systems of record, through the same governance, the same audit trail, the same role-based access your enterprise already trusts.
The conversational surface is AI. The transaction engine is deterministic software. That split is the reason our work ships to production in regulated environments while agent-first pilots stall in the security review.
Salesforce has just announced Headless 360 — the entire Salesforce, Agentforce, and Slack surface exposed as APIs, MCP, and CLI.
ServiceNow has committed to an AI-first employee surface. Microsoft's Copilot push is a different-shaped answer to the same observation. Across the incumbent stack, the premise that the system of record and the surface that fronts it are different problems is now industry consensus. The infrastructure that makes a decoupled surface possible is being built — on the vendors' timeline, not yours.
The open question for your enterprise is what consumes those newly exposed APIs. The mainstream answer the vendors are pushing is autonomous agents — systems that reason, decide, and commit transactions on their own. It is also the answer your compliance team at a bank, a hospital system, or a regulated manufacturer will struggle to approve for production any time soon. A decoupled surface without a deterministic execution layer is a demo, not a deployment.
There is another path. The same APIs, consumed deterministically, through conversational applications your security and audit teams already know how to approve. The question for the next eighteen months is not whether to decouple the surface — the vendors have already decided that for you. It is what sits on top of those APIs the moment a user asks it to commit a transaction.
Enterprises that move now reach production ahead of their peers, build operational muscle on the new architecture while there is still room to learn, and become the references that define the category. The ones who wait will be choosing from a supply side that is better organised, more expensive, and booked.
Six architectural shifts that follow from replacing the surface rather than augmenting it.
Employees speak or type to a single conversational surface. The platform routes their intent to the right backend operation or returns a synthesized answer drawn from your knowledge corpus. No menus to navigate. No application to "be in." No sequence of logins.
Switching expense providers from Concur to Navan does not touch the employee. Your integration team re-points the adapter; the employee sees nothing different. Vendor lock-in inverts: switching cost moves from your user base to your integration team — an order of magnitude smaller, and a cost you can plan around.
Free-text conversation never triggers spend, grants access, or commits a transaction. Every sensitive action runs through a controlled application workflow with role-based access, policy enforcement, approval chains, and full audit trails. Permissions inherit from your existing identity provider — Azure AD, Okta, OCI IAM, any SAML or OIDC source.
Every AI response and every workflow renders in your enterprise's brand. Brand and content integrity are guaranteed regardless of what the AI generates, because the rendering layer never hands control of the surface to the model.
WCAG compliance is built into the foundation. Every screen, every workflow, every AI-generated response inherits accessibility from the platform. For regulated industries and public-sector obligations, this is structural, not a project bolted on later.
Deploys inside your existing cloud infrastructure — OCI, AWS, or Azure — under your existing security and compliance posture. No new SaaS vendor in your data path. Identity federates through your SSO. LLM inference runs against your chosen provider under your model governance and data-residency rules.
Every engagement runs on the same proprietary three-layer architecture. What changes is where it lands in your estate.
Customer-facing conversational commerce for enterprises whose transactions live in an ERP, a PIM, a CRM, a billing system, or a custom order-management platform. The conversational surface sits in front of those systems; checkout, account management, service requests, and fulfillment run as deterministic workflows against the systems of record.
One conversational surface across the employee stack — HRIS, ITSM, expense, procurement, travel, facilities, LMS. Time off, laptop tickets, expense submissions, travel bookings, policy questions, and the long tail of internal requests collapse into a single dialog. Systems of record stay where they are; the surface no longer matches them one-to-one.
Conversational applications over a specific domain corpus — regulatory filings, clinical documentation, policy libraries, contract archives, engineering standards, product catalogues. Employees ask natural-language questions; the platform returns sourced, branded, auditable answers with structured actions. The corpus stays in your tenant; synthesis runs against your chosen LLM under your governance.
Larger, multi-surface engagements where the conversational layer spans several systems, several divisions, or both — with shared identity, shared brand, shared audit, shared orchestration. Scoped as a program, not a project. Delivered in phased releases against the same five-month cadence, with each phase independently productionisable.
Every interaction is deterministic application code executing beneath a brand-consistent conversational surface.
"Book me on the Tuesday flight to Frankfurt and mark me OOO that afternoon."
The platform routes the intent, executes the travel booking against your travel system, updates the calendar, sets the out-of-office. Three systems coordinated, no tool switching. Every API call logged, every transaction auditable. The employee never sees Concur, never sees Outlook, never sees whatever you use for OOO. They had a conversation; the systems coordinated.
"What's our policy on paid volunteer time?"
The platform reads across your policy corpus, synthesizes the answer, and renders it as a branded response with a "Request Volunteer Time" action. The employee taps the action; the platform launches the time-off workflow against your HRIS. Question to action, in one continuous experience. The AI never executes. The applications execute.
"Dispute the charge from last Tuesday on the Emerald account and refund it if it's inside the service window."
The platform parses the intent, retrieves the transaction from the ledger, checks service-window eligibility through policy code, opens a dispute case in the case-management system, and — only if every policy check passes — commits the refund through the payments rail under the authorised agent's role. Every step is application code. Every step is audited. The conversation is natural; the execution is the same deterministic path your compliance team has already signed off.
Every engagement ships on the same architecture: theme tokens, a component sprite, and deterministic primitives.
The theme tokens define your brand — colour, typography, spacing, rendering rules — as structured data, compiled once and inherited by every surface. The component sprite is a registry of validated, accessible UI primitives. The deterministic primitives are the application-code workflows that execute against your systems of record.
The AI orchestrates the dialog. The primitives execute the transaction. The sprite renders the response. The theme guarantees the brand. Nothing the model generates reaches a system of record without passing through a deterministic primitive under your governance.
The platform absorbs sixty to seventy percent of what would otherwise be custom engineering on every engagement. That arbitrage is why a senior, architecture-led team of two to three people delivers in five months what a traditional transformation program typically requires twelve to eighteen months and a fifteen-person team to produce.
A conversational-first user interface system that fundamentally reinvents application architecture by eliminating interface learning requirements. Users interact through natural language as the primary interface, with the system intelligently invoking graphical user interface tools when task-appropriate.
This inversion of traditional GUI-centric design democratizes software access. Fast-path sequential processing ensures rapid routing to transactional interfaces.
An electronic commerce system that replaces traditional search-based product discovery with natural customer conversation within a unified visual stream interface — high-fidelity product cards, conversational interaction, and shopping cart functionality in a single continuous scroll.
Complete product catalog knowledge embedded in each prompt enables natural, accurate recommendation across the full catalog.
The reference deployment is a guest-facing conversational application in luxury hospitality, running on live Oracle infrastructure at zenpacorlando.com. You will see structured commerce, knowledge synthesis, and open conversation blend into one continuous experience — all deterministic, all auditable, all brand-consistent.
The vertical changes. The engine doesn't.
The same architecture that gives a luxury hotel guest one conversation across the property gives your employee one conversation across your enterprise, or your customer one conversation across your commerce estate.
Cordiant engagements are bespoke implementations delivered from India. Our engineering organisation — including leadership — is distributed across India. We are a fully remote company, headquartered in Bengaluru, and the founder personally runs every engagement from first briefing to first system live. This is our native operating model — the cost structure, the scale posture, the architectural discipline — all refined across two decades of Fortune 5000 services work as Cordiant.
The difference from every other India-based services firm is the platform. The three-layer architecture absorbs sixty to seventy percent of what would otherwise be custom engineering on every engagement. A senior, architecture-led team of two to three people ships what larger programs require fifteen-person teams and twelve to eighteen months to produce.
Deployment runs inside your existing cloud infrastructure — OCI, AWS, or Azure — under your existing security and compliance posture. No new SaaS vendor in your data path. No data leaves your tenant. Identity federates through your SSO. LLM inference runs against your chosen provider — Azure OpenAI in tenant, Bedrock, or a self-hosted model — under your model governance and your data-residency rules.
Five months from kickoff to first system live. The first system is the hardest because it includes the brand setup, the identity integration, and the governance foundation. The second system reuses that foundation. By the fifth or sixth, each new system is primarily a configuration exercise against patterns we have already built for you.
Pricing is transparent because transparency is the wedge. Every Cordiant engagement is milestone-billed against fixed deliverables. Source code, documentation, and full knowledge transfer are delivered under a perpetual internal-use license — your team can run, extend, operate, and exit the platform at any point, for use within your entity. The license does not extend to reselling, sublicensing, or using the platform to service third parties.
Opaque pricing is the default in enterprise AI services. It benefits the seller and taxes the buyer. We publish because the comparison we want is on architecture, on delivery discipline, on what ships to production — not on who can extract the largest scope from a procurement cycle.
A thirty- to forty-five-minute briefing with Cordiant leadership. We will walk the architecture, show the reference deployment handling a full commerce and concierge lifecycle, and propose a first-system scope tailored to a high-friction surface in your enterprise.
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