ITRex’s AI practice is led by Kirill Stashevsky, CTO, who brings more than 20 years of experience in software engineering and enterprise transformation. He is supported by senior AI, Gen AI, data, cloud, and software engineers who have built and integrated intelligent systems across healthcare, finance, manufacturing, retail, logistics, and other data-intensive sectors.
Our AI integration services for enterprise workflows span AI and Gen AI readiness assessment, architecture design, data engineering, model deployment and validation, and ongoing MLOps/LLMOps. We define what your AI system needs to read, what it may change, how it authenticates, where human approval belongs, and how quality will be measured before real users interact with it.
We design AI integrations with both initial and ongoing costs in mind, balancing model usage, infrastructure, maintenance, and anticipated scale. AI integration consulting and architecture engagements typically cost $15,000-35,000, focused PoCs run $45,000-100,000, and multi-system implementations begin at $100,000, with estimates covering both development and recurring costs.
Our AI integration consultants examine the workflow, current architecture, available data, integration constraints, and organizational readiness. Together, we define baseline performance, the business outcome to improve, and the metrics that will decide whether the initiative advances. If AI is not the right answer—or the data cannot support the proposed use case—we say so before development begins.
The ITRex architects identify systems of record, data owners, authentication boundaries, latency requirements, and downstream dependencies. For Gen AI solutions, this includes knowledge sources, retrieval permissions, context boundaries, and the actions a model or agent may take. The result of the enterprise AI integration consulting engagement is a clear map of what the solution reads, writes, recommends, and escalates.
We compare proprietary services, open-weight models, and existing client assets against accuracy, speed, privacy, maintainability, and cost requirements. The AI integration may use REST or GraphQL APIs, webhooks, message queues, event-driven services, database connectors, tools exposed through MCP, or an anti-corruption layer around a legacy system. The choice follows the workflow, not a preferred vendor.
Our AI integration company starts with one bounded workflow and real representative data. Functional testing is combined with model-specific evaluation: accuracy, precision and recall, groundedness, latency, robustness, cost per transaction, and failure behavior where relevant. Security testing and red teaming cover access-control bypasses, data leakage, prompt injection, unsafe tool use, and other misuse scenarios relevant to the system.
The solution is introduced through staged releases, shadow mode, canary deployment, or human-in-the-loop operation, depending on risk. Before expanding access, we test the integration under production-like loads and confirm that quality, latency, and reliability meet the agreed thresholds. Our AI integration services also cover observability, fallback behavior, audit logs, rate limits, rollback paths, and documentation for the teams that will operate the system.
After launch, MLOps or LLMOps pipelines track model quality, data and concept drift, retrieval performance, latency, availability, and inference spend. We preserve core regression sets for consistent release-to-release comparisons and add new test cases as workflows, risks, and production data change. As your AI integration company, ITRex can investigate performance drops, tune retrieval and models, and manage production updates. Your team can retain operations, share them with our team, or take full ownership after a structured knowledge transfer.
Enterprise AI integration connects a model or AI-enabled application to the data, software, controls, and workflows needed to produce a business outcome. The work typically covers validating the use case and selecting the model, preparing data and connecting systems, managing access, adapting the interface, testing and monitoring the solution, and supporting it after launch. For example, integrating an LLM into a CRM system involves more than sending prompts to an API. The system may need to retrieve account history, respect field-level permissions, generate a recommendation, request approval from a sales representative, write the approved action back to the CRM, and record the exchange for audit and evaluation. That surrounding architecture turns an AI demo into a usable enterprise capability.
Our AI integration company first maps the system’s interfaces, data model, authentication methods, release constraints, and role in daily operations. Where stable APIs exist, we use them. Where they do not, options include a secure integration layer, database views, message queues, event capture, robotic process automation, or a narrowly scoped adapter that exposes only the functions the AI solution needs. Replacement is not assumed. A manufacturer, for instance, may retain its ERP as the system of record while an AI service forecasts material demand and returns recommendations through an API. A human planner reviews the recommendation before any approved change reaches the ERP. This staged pattern adds AI capabilities without giving a new model uncontrolled access to a critical system.
An AI API gives an application access to a model. AI integration services make that model reliable and useful within a specific product or business process. It covers the work around the API: retrieving authorized data, translating application context into model inputs, validating outputs, connecting tools, managing failures, logging activity, monitoring quality and cost, and assigning human responsibility. A direct API connection may be sufficient for a low-risk prototype. A customer support assistant connected to a CRM, knowledge base, billing platform, and ticketing system needs a broader integration architecture. AI integration for SaaS platforms may also require tenant isolation, OAuth flows, usage quotas, webhook handling, configurable permissions, and monitoring by customer accounts. The right scope of an enterprise AI integration project depends on what the model can access, what actions it can take, and what happens when its output is wrong.
The safest path is usually incremental. Our AI integration consultants begin with a bounded workflow, document its current performance, and identify which steps artificial intelligence can support or automate without removing necessary oversight. The integration is then tested against representative data outside the production path. Depending on the risk, the system may enter shadow mode, where it produces outputs without acting on them; operate with mandatory human approval; or reach only a small group before a wider rollout. Fallback behavior, observability, rollback paths, and rate limits are defined before release. Existing systems remain the source of truth until the integration meets the agreed acceptance criteria. This approach is particularly useful for legacy modernization, where replacing the underlying software would introduce more risk and cost than adding carefully isolated AI functionality.
For companies asking how long enterprise AI integration takes, the timeline depends on the scope. We typically complete a focused readiness or architecture engagement in two to four weeks and build an AI integration PoC for a single workflow in six to twelve weeks. Deploying a production solution across several systems usually takes three to nine months, especially when the project requires custom data pipelines, security reviews, interface updates, and operational handover. Data access and readiness often shape the timeline more than model development. Clients can accelerate delivery by providing representative data, API documentation, evaluation criteria, and access to system owners from the start. After discovery, we define the roadmap and break larger programs into releases so the client can test each increment before committing to the remaining scope.
The AI integration cost depends on the project’s scope and production requirements. ITRex typically charges $15,000-35,000 for an AI readiness assessment, feasibility study, or integration architecture engagement and $45,000-100,000 for a PoC focused on one production workflow. Full enterprise AI integration projects usually start at $100,000. Connecting more systems, processing larger data volumes, customizing models, meeting compliance requirements, supporting multiple user roles, and setting stricter availability targets will raise the final budget. Our guide to artificial intelligence costs explains how the solution type and technical requirements shape project estimates.
Model and API fees make up only part of the total. Companies must also budget for data preparation, application updates, security controls, quality evaluation, observability, infrastructure, and ongoing model operations. These expenses can exceed the cost of model access, particularly in enterprise deployments. During planning, ITRex prepares AI integration project cost estimates that cover both implementation and expected operating expenses, then connects the investment to measurable acceptance criteria. For a closer look at model usage, infrastructure, development, and maintenance expenses, see our guide to calculating the cost of Gen AI.
ITRex is a vendor-agnostic AI integration agency. We work with proprietary models and platforms from OpenAI, Anthropic, Google, Microsoft, AWS, and Cohere, as well as selected open-weight models from the Llama and Mistral families. We compare the available options based on output quality, privacy, latency, throughput, deployment requirements, integration effort, and total operating cost. We also do not assume that every business problem requires a large language model. Depending on the use case, a predictive ML model, computer vision system, rules-based component, or small language model (SLM) may deliver more accurate results with lower latency and operating costs. Complex solutions can combine several approaches: for example, an LLM may interpret a request, a predictive model may generate a forecast, and business rules may determine whether the system can act on the result. When multiple language models are involved, routing directs each request to the model that offers the right balance of quality, speed, and cost.
Our AI integration services cover both customer-facing products and internal operations. We build data and analytics solutions for segmentation, forecasting, customer intelligence, and next-best-action recommendations; automate document-heavy and multi-step business workflows; and connect conversational assistants to approved knowledge, CRM records, product data, and support systems. We also integrate recommendation models with eCommerce platforms and embed AI search, content generation, copilots, agents, and analytics into multi-tenant SaaS products.
The architecture depends on the business task. A customer data project may rely on pipelines, entity resolution, semantic layers, and predictive models, while a custom AI chatbot may combine an LLM with retrieval, permissions, citations, tool access, and human escalation. Workflow automation may use document processing, business rules, and predictive or generative models to complete routine steps and send exceptions to employees for review.
Evaluate an AI integration service provider by looking at production systems it has delivered for companies with similar data, workflows, and regulatory constraints. A strong team should bring together AI, data, cloud, software engineering, UX, QA, and security specialists. It should also explain how it will connect AI to your systems, select models against documented criteria, protect sensitive data, handle failures, and measure quality, latency, reliability, security, and cost.
A qualified AI integration consulting company should provide more than an implementation plan. Look for access controls, audit trails, human oversight, and support for relevant regulatory obligations, as well as MLOps or LLMOps practices covering monitoring, regression testing, versioning, and controlled updates. The provider should estimate both implementation and operating costs and define a realistic path from PoC to production, including ownership, handover, and post-launch support. Be cautious if an AI integration agency recommends a model before reviewing your use case and data, treats API access as the entire architecture, cannot explain how outputs will be evaluated, or presents a PoC as proof that a solution is ready for production. Whether you are hiring an AI integration company for a SaaS platform, workflow automation, data analytics, customer support, or eCommerce personalization, choose one whose production experience and operating model fit your systems, risks, and expected outcomes.
