Welcome to ChatINT.ai

What will the world look like once Trainable Integrations are the norm?

Enterprises will treat integration as an always-on capability, not a fragile project. When systems evolve, integrations are retrained. Teams no longer pause to rebuild or remap — they simply retrain existing models against new specifications. Integration stops being a bottleneck and becomes the fabric of scale: every new system, every new API, every new data stream connects once and keeps pace with change forever.

How will this change how companies build and maintain integrations?

Today, every schema or contract change forces fragile coordination: providers freeze releases, consumers scramble to rewrite code, and both sides suffer version churn.

With a Trainable API, that cycle ends. Consumers train the integration to produce the structure they need, and that trained model holds steady even as providers rename fields, alter types, or restructure payloads.

Providers no longer ship parallel versions just to keep consumers alive. Consumers no longer rebuild every time a field shifts. Retraining absorbs these changes in minutes, extending stability as long as the underlying meaning of the data remains constant — and in practice, semantics almost never change.

The result is organizational velocity: providers move forward without waiting, consumers remain stable without rewrites, and integrations stay aligned through rapid, repeatable retraining. Teams focus on delivering value, knowing adaptation is a controlled step, not a disruptive rebuild.

What’s the “holy sh*t” moment for customers?

It happens the first time someone sees a live retraining. An API or schema changes, and in minutes the integration adapts. What once required weeks of breakage, remapping, and retesting collapses into a single controlled step.

That moment makes the shift unavoidable: consumers keep their contracts stable, providers evolve without parallel versions, and the rebuild tax disappears. Once retrainability is seen in action, no enterprise will ever go back to static integrations.

Duane Lall has solved the core technical and architectural challenges of static integration by designing, building, and delivering the ChatINT.ai platform. The platform is functional, demo-ready, and proves that a trainable, adaptive model for enterprise integration is not just a concept, but an operational reality.

Key Accomplishments

• Delivered a Functional Platform: Built the core platform, which is currently demo-ready. It can perform a live retraining, adapting to real API and schema changes in minutes—demonstrating the solution to the market's primary integration pain point.
• Shipped the Core Technical Constructs: Defined and implemented the foundational intellectual property of the company:
  • Trainable Integrations: Dynamic, self-adapting connections that evolve over time without requiring constant manual rework.
  • The Trainable API: A specialized capability allowing data consumers to define the exact structure of API responses they need, drastically reducing integration time and expense.
  • Living Domain Contracts: Are continuously retrained models that keep integrations aligned as systems evolve.
• Established Market & Business Viability: Developed the comprehensive Total Addressable Market (TAM) model, which shows the 2024 global integration spend at $350B–$484B. The model projects a potential ~$1T market by 2030, driven by an explicit AI/MCP additive layer.
• Outlined Initial Market Engagement & Roadmap: Defined the immediate next steps for engaging the market, focusing on early-access demos for enterprises with the most complex integration challenges. This is supported by a clear development roadmap for achieving SOC2 compliance and performance hardening to meet enterprise readiness requirements.

If you have no users yet, how do you know this will work?

Our confidence comes from having already built a functional and demo-ready core platform that is rigorously validated through a multi-layered testing approach, proving that our solution is not just a concept but an operational reality.

From Concept to Capability

The platform's key capabilities are already operational. We can generate adaptive integration models from artifacts and automatically retrain them when interfaces change. The entire system is validated by a robust suite of over 2,000 automated tests, with several hundred dedicated specifically to the rigorous training and execution of our integration models.

Integrated Validation for Accuracy and Stability

Each model produced by ChatINT.ai is automatically verified through a rigorous evaluation process that measures both accuracy and stability. Validation is embedded directly into model generation, so performance is assessed as part of creation rather than as a separate step. The result is a dependable integration model that delivers consistent outcomes from the moment it’s deployed.

The most compelling external evidence is our ability to demonstrate a live retraining session. In this demo, we can show the platform adapting to real-time API and schema changes in minutes—a process that typically takes weeks of manual re-engineering and testing.

A Clear Path to Enterprise Readiness

Our immediate roadmap is focused on bridging the gap from a functional platform to a fully enterprise-grade solution. The next steps are clear and deliberate: achieving SOC2 compliance, completing extensive performance hardening to handle large-scale workloads, and engaging in targeted demos with enterprises facing the most complex integration challenges to validate our solution against the market's hardest problems.

What are the strongest signals this market is ready?

Integration pain is universal and spend is already enormous and growing. AI and MCP add a new, fast-growing surface area—driving more endpoints, more change, and a need for trainability.

The TAM analysis shows $350B–$484B in 2024 rising to ~$0.77T–$1.18T by 2030, with an explicit AI/MCP additive layer (Low→High scenarios). Industry sentiment forecasts rapid agent adoption, validating endpoint and change growth.

How will you make money with Trainable Integrations?

We use a multi-channel model combining direct enterprise SaaS subscriptions with a partner-led managed services offering. Revenue is based on annual platform access, scales with customer usage via value-based tiers, and is supplemented by a high-margin channel for outsourced integration services.

1. Core Platform Subscription

The foundation is an annual SaaS subscription giving enterprises direct access to the ChatINT.ai platform, including the runtime engine and core tools to create and manage their integrations. This provides predictable, recurring revenue.

2. Value-Based Scaling & Add-Ons

Subscription tiers scale with value metrics like the number of active integrations, data volume, and retraining frequency. Premium capabilities, like the Trainable API, can be licensed on a per-integration basis for complex use cases. This model empowers customers to build their own internal Enterprise Translation Service (ETS).

3. Managed Services & Partner Ecosystem

To capture customers who prefer a fully managed solution, we will enable a partner ecosystem of certified Systems Integrators (SIs). These partners will use our platform to offer "Trainable Services," handling all integration creation and maintenance on the client's behalf. This opens a high-margin channel that taps into the massive system integration services market.

What other markets or products can this expand into?

Regulated/industry workloads (FHIR, FIX, HL7, NIEM, X12), IoT integration platforms, and AI/MCP-enabled integration layers across enterprise domains.

These domains have high change rates and strict semantics—prime candidates for Living Domain Contracts and consumer-shaped Trainable API responses.

What makes your approach defensible long-term?

True trainability requires an architecture built for it from the start. ChatINT.ai is purpose-built around retraining, with proprietary deterministic models that evolve as systems change. This cannot be retrofitted into static platforms, which is why we lead the inevitable shift to adaptive integration.

The Incumbent's Dilemma: Limits of Static AI

Legacy integration platforms were architected for static mappings. When they add AI, it accelerates existing processes rather than enabling adaptation. This creates two structural limits:

1. Static Outputs: AI layered on top of static models generates brittle code and mappings. More code means more maintenance, reinforcing the rebuild cycle.
2. Business Model Inertia: Large portions of incumbent revenue depend on rebuilds and professional services. A retrainable approach reduces that friction, creating a direct conflict with their incentives.

Our Purpose-Built Advantage: No-Code, Deterministic Models

ChatINT.ai was designed from day one for continuous change. We use proprietary algorithms to create deterministic, retrainable integration models. Every model is no-code and runtime-managed, delivering reliable outcomes without creating new maintenance debt.

Foundational constructs like Living Domain Contracts and the Trainable API anchor this architecture. They ensure that integrations evolve through retraining, not rebuilding — making ChatINT.ai the definitive solution for the next paradigm of interoperability.

Complementary to Existing Platforms, Replacement for Fragile Translation Layers

ChatINT.ai extends the value of iPaaS, API gateways, and workflow orchestrators by adding trainability. Instead of maintaining schema mappings, brittle transformation code, or static DSLs, teams rely on adaptive models that remain aligned as systems evolve.

This shift removes the need for fragile translation layers, drastically simplifying integration for both enterprise developers and existing tools. The impact is enormous: every hour saved from debugging or remapping is redirected to delivering features and business value. Across thousands of integrations per enterprise and millions across industries, that recovery equals billions of dollars in reclaimed engineering effort.

ChatINT.ai introduces the missing layer in enterprise systems — a foundation that adapts with change, sustains reliability, and establishes a new category.

What is a Living Domain Contract and why does it matter?

A Living Domain Contract is a continuously updated model of the data being integrated (APIs, events, queues, files). It evolves through retraining as systems change, keeping integrations aligned without rebuilds.

Contracts are realized and enforced through adaptive integration models that are retrained when domains or artifacts shift. This replaces static specifications that expire on publication.

What is the strategic plan for a $25M investment?

A $25M investment validates inevitability by embedding ChatINT.ai inside the organizations where Trainable Integrations are most urgent and most visible. These include Global 2000 enterprises with dense integration networks, government agencies facing evolving interoperability mandates, and the investment arms of corporations and sovereign funds actively seeking disruptive infrastructure. The strategy is deliberate: secure a small set of anchor accounts, solve their live integration problems through Forward Deployed Engineering, and demonstrate that Trainable Integrations scale in environments representative of the entire market. Success in these accounts establishes inevitability — and defines the category.

1. Opportunity Identification & Engagement: Build a dedicated team to map and engage with a focused set of enterprises, government agencies, and strategic investment groups. The priority is precision, not volume: securing a small set of anchor partners where success demonstrates market inevitability.

2. Forward Deployed Engineering (FDE) Program: Deploy engineers directly into partner environments to solve live integration problems with ChatINT.ai. This program ensures immediate value, accelerates adoption, and generates continuous market-driven feedback that shapes the roadmap.

3. Beta Partnerships with Strategic Accounts: Fund targeted Beta Programs with global enterprises and public-sector organizations to validate retrainability across high-complexity integration scenarios, including regulated and mission-critical domains.

4. Platform Scaling for Enterprise Readiness: Invest in security and compliance (SOC2 and beyond), performance hardening, and engineering capacity to support enterprise-grade workloads and expand capabilities identified through FDE and Beta engagements.

5. Operational Infrastructure: Build out Finance, Legal, Marketing, and HR functions to support rapid scaling and global deployment, ensuring the platform and company can operate at the standard required by large enterprises and government agencies.

Is $25M enough to achieve your goals?

$25M is the capital required to validate inevitability. With this investment, ChatINT.ai will achieve enterprise-grade readiness, embed with strategic accounts, and prove the retrainable integration model in environments where success is both highly visible and broadly representative.

The $25M plan is designed as a focused validation stage, not the full capitalization of the market. Its purpose is to demonstrate inevitability in practice and establish the reference points that unlock larger rounds of capital. The allocation is structured around three priorities:

1. Enterprise-Grade Platform: Achieve SOC2 compliance, implement performance hardening, and expand engineering capacity to support massive workloads. These steps ensure the platform meets the requirements of Global 2000 enterprises and government agencies.

2. Strategic Engagements: Deploy Forward Deployed Engineers and fund Beta Programs with a small set of high-value partners — Global 2000 enterprises, government agencies with evolving interoperability mandates, and the investment arms of corporations and sovereign funds actively backing new infrastructure. Only a limited number of these engagements are needed to validate inevitability, because their complexity and visibility make them market-defining.

3. Operational Infrastructure: Build the organizational foundation — Finance, Legal, Marketing, HR — required to operate at the level expected by large enterprises and government. This ensures the company can scale credibility alongside technology.

This level of capital is sufficient to prove the paradigm shift and establish ChatINT.ai as the anchor of a new market. Scaling across industries and regions will require additional investment, but that next stage of capital follows naturally once inevitability has been validated in the field.

What should an investor do next if they’re interested?

The first step is to schedule a live demo by contacting us at investors@chatint.ai. This initial meeting will be followed by a detailed discussion of our initial go-to-market strategy, the need for a Forward Deployed Engineering model, and the initial financial plan.

Engage with us to validate the technical solution and discuss how the FDE program will accelerate adoption within high-value enterprise accounts, creating a strong feedback loop for product dominance and long-term defensibility.