Generative AI
Integration Services
Make AI a natural extension of your business processes. Our generative AI integration services help you embed AI into your existing software and workflows to automate handoffs between teams and systems.
Less switching between tools. More customer value.
Production AI outcomes across fintech, retail, and social platforms
faster provider integration
0
X
monthly API calls
0
+
faster media loading
0
%
Where most AI integrations break
98% of companies experiment with AI. Only 39% ever deploy their AI implementations. The rest run into one of the four roadblocks.
AI becomes another place people have to work
CRM
Copy
AI
Copy
Email
How we solve it
Integrate AI natively into your everyday workflow tools to allow automated system records updates with only user confirmation
CRM
AI built in
Email
AI lacks business context
Question
AI
No access
Guess
CRM
Contracts
Pricing
Connect AI to the business systems through retrieval strictly scoped to each user's existing access levels
Question
AI
Permission-aware
More accurate answer
CRM
Contracts
Pricing
Every new integration becomes another custom project
System A
Custom integration
System B
Make new system addition a matter of a quick configuration due to the upfront development of one reusable integration layer
System A
Reusable integration layer
System B
AI never becomes operational
Event occurs
Manual step
AI used
Manual review
Outcome updated
Architect event-driven systems that trigger AI automatically off your workflow events, reducing manual interventions
Event occurs
AI triggered
Manual review for critical decisions (if needed)
Outcome updated
Who benefits from enterprise AI integration services?
AI integration services make sense when you already know where artificial intelligence could create value in your case, but you realized that getting it into your business is proving harder than expected.
CTOs and CEOs who need to
- Connect AI to enterprise applications
- Protect existing architecture
- Control data access
- Scale without creating integration debt
You need enterprise AI integration services to work with legacy systems that have strict security requirements, multiple integrations with systems, or architecture limitations that don’t allow you to rip out working systems just to make room for AI.
Transformation leaders who need to
- Scale beyond pilots
- Automate high-friction processes
- Prove ROI
- Coordinate AI adoption across teams
You have promising AI use cases that haven't yet made it into everyday operations. You need AI business solutions that prove value, scale what works, and give business and technology teams a clear path from experiment to adoption.
Engineering managers who need to
- Productionize an AI prototype
- Connect APIs and data sources
- Build retrieval or agent workflows
- Extend your engineering capacity
Your engineers can prototype an AI capability, but connecting it to production data, APIs, and downstream workflows, setting up permissions and the rest of production operations, exceeds internal capacity.
Product leaders who need to
- Add AI features to an existing product
- Work with your current backend and data
- Improve customer workflows
- Move from feature concept to production
You want to introduce customer-centric AI features that won’t break or require refactoring existing backend, data model, or UX that weren't designed to power generative AI functionality.
Signs your business is ready for AI integration
Operational inefficiency
Your team spends hours copying information between systems, checking documents, preparing reports, or routing requests to complete routine tasks that could be handled through workflow automation.
Pilot-to-production gap
You proved the use case in a controlled environment, but once it enters the production environment and real workflows, something breaks: permissions, response accuracy, or system integration.
Lack of business context
Your model gives less accurate or context-poor responses because it can’t access customer records and other relevant information stored across multiple distributed systems.
Legacy constraint
Legacy systems run critical parts of the business, but outdated APIs and rigid, brittle architecture complicate LLM integration, requiring significant rework.
AI governance, compliance & security
AI processing of sensitive customer or financial data demands tighter controls, governance, and security measures built into the workflow from the start to prevent any privacy risks or security flaws from reaching production.
HAVE ONE OF THESE PROBLEMS IN YOUR STACK?
What we can help you connect AI to
Your existing stack contains years of business knowledge and operational logic that are more valuable than what AI can create on its own. So, forcing or rebuilding working infrastructure to introduce AI is not the path to value. We offer AI integration consulting services that deliver real business outcomes through low-risk implementation.
Business systems
CRM · ERP · HR · Finance
Connect AI to business-critical systems that contain operational data to enable relevant information retrieval and record updates across the organization for faster decision-making.
Less switching & faster action
Enterprise knowledge
SharePoint · Internal documentation · Knowledge bases
Give LLMs controlled access to the internal documents, procedures, policies, and institutional knowledge, scoped to each user’s access rights.
Faster access to knowledge
Operational workflows
Zendesk · Salesforce Service Cloud · ServiceNow · Compliance software
Embed AI into daily workflows to automate repetitive steps involved in customer requests and service operations and resolve issues faster.
Less manual work & faster processes
Data & integration infrastructure
APIs · Data warehouses · Vector databases · Internal services
Build a middleware layer with our AI integration specialists that lets AI access the right data and services while making it easier to add new use cases.
Better AI context & easier scaling
Don’t see your system here? We work with custom, proprietary, and legacy systems too.
AI business solutions we can build into your operations
AI-powered enterprise search
Put business knowledge to work with AI that retrieves answers from your docs and systems and provides accurate answers whenever your team needs them.
AI workflow automation
Automate the repetitive steps between a request coming in and being resolved using AI that performs extraction, routing, and downstream updates on its own.
AI copilots
Give your teams and your customers a faster way to find information and complete tasks through an AI interface that works as an extension of the tools they already use.
Embedded GenAI
Build intelligence directly into the CRMs, ERPs, and portals to make AI-powered features part of the core user experience with zero context switching.
AI agents & task orchestration
Coordinate multistep work with AI agents that can gather information and use business tools to perform routine tasks on your behalf, engaging people only when required.
Your use case
Have a different use case in mind? We can create AI capabilities tailored to your specific workflows and systems to solve your unique business problems.
What happens after you choose us for AI system integration services
Successful AI implementation depends on close and transparent cooperation between your teams and ours. After we agree on the delivery plan and engagement model, we keep you close to key project decisions and review progress against business needs to course-correct early if needed. That reduces the risk of costly surprises later in the project and gives you complete control over the project’s direction and speed.
Discovery
Validate and scope the use case.
We map the workflow, systems, data, governance and security controls, validate whether the proposed AI use case is technically and operationally viable, define the success criteria, and identify what should be automated vs kept under human control.
You get: A scoped use case, success criteria, delivery plan
We map the workflow, systems, data, governance and security controls, validate whether the proposed AI use case is technically and operationally viable, define the success criteria, and identify what should be automated vs kept under human control.
You get: A scoped use case, success criteria, delivery plan
1-2 weeks
01
STEP
02
STEP
Architecture
Design the path to production.
We define the integration points, data flows, AI architecture, security controls, and fallback mechanisms needed for the solution to work in your environment.
You get: A production architecture and implementation plan
We define the integration points, data flows, AI architecture, security controls, and fallback mechanisms needed for the solution to work in your environment.
You get: A production architecture and implementation plan
3-4 weeks
Build
Ship the features in phases.
We build the integrations and AI capabilities in incremental steps, testing them against real workflows and validating accuracy, performance, and failure handling abilities.
You get: Working functionality
We build the integrations and AI capabilities in incremental steps, testing them against real workflows and validating accuracy, performance, and failure handling abilities.
You get: Working functionality
8-12 weeks
03
STEP
04
STEP
Handoff
Prepare the solution for your team to take over.
We document the solution, transfer knowledge, establish operational ownership, and provide the agreed post-launch support.
You get: Documented, operable solution and clear ownership
We document the solution, transfer knowledge, establish operational ownership, and provide the agreed post-launch support.
You get: Documented, operable solution and clear ownership
12+ weeks
01
STEP
Discovery
Validate and scope the use case.
We map the workflow, systems, data, governance and security controls, validate whether the proposed AI use case is technically and operationally viable, define the success criteria, and identify what should be automated vs kept under human control.
You get: A scoped use case, success criteria, delivery plan
1-2 weeks
We map the workflow, systems, data, governance and security controls, validate whether the proposed AI use case is technically and operationally viable, define the success criteria, and identify what should be automated vs kept under human control.
You get: A scoped use case, success criteria, delivery plan
02
STEP
Architecture
Design the path to production.
We define the integration points, data flows, AI architecture, security controls, and fallback mechanisms needed for the solution to work in your environment.
You get: A production architecture and implementation plan
3-4 weeks
We define the integration points, data flows, AI architecture, security controls, and fallback mechanisms needed for the solution to work in your environment.
You get: A production architecture and implementation plan
03
STEP
Build
Ship the features in phases.
We build the integrations and AI capabilities in incremental steps, testing them against real workflows and validating accuracy, performance, and failure handling abilities.
You get: Working functionality
8-12 weeks
We build the integrations and AI capabilities in incremental steps, testing them against real workflows and validating accuracy, performance, and failure handling abilities.
You get: Working functionality
04
STEP
Handoff
Prepare the solution for your team to take over.
We document the solution, transfer knowledge, establish operational ownership, and provide the agreed post-launch support.
You get: Documented, operable solution and clear ownership
12+ weeks
We document the solution, transfer knowledge, establish operational ownership, and provide the agreed post-launch support.
You get: Documented, operable solution and clear ownership
Our AI integration technology arsenal
Models & orchestration
- OpenAI GPT
- Claude
- Gemini
- Llama
RAG & data retrieval
- LangChain
- LlamaIndex
- Haystack
- Hugging Face
- Qdrant
- OpenSearch
Cloud and AI platforms
- AWS
- Microsoft Azure
- Google Cloud
- Amazon EKS
AI frameworks
- LangChain
- LangGraph
- CrewAI
- PyTorch
- Hugging Face
- ONNX
- TorchServe
System integrations
- API gateways
- Middleware
- Event-driven services
- API-first architecture
Production AI & MLOps
- Docker
- Kubernetes
- Kubeflow
- Apache Kafka
- Apache Spark
Our success stories worth reading
AI photo restoration that fixed a broken third-party capability
Social networking · Reekolect
Reekolect came to Aimprosoft with a concept for a social platform that could preserve and enhance family memories. We built the platform and replaced a poorly implemented third-party photo restoration feature with an AI-based solution, integrating it directly into the product experience.
70%
faster media load times
AI-driven payment routing
Fintech · NDA
A payment platform needed real-time routing decisions that won’t disrupt its live revenue flow. We added an ML layer around the existing C#/.NET rules engine, introduced continuous model monitoring, and integrated new acquirer APIs directly.
faster provider integration
0
x
new acquirer connections per month
0
vs 1
monthly API calls
0
k+
acquirers under ML-based routing
0
+
Real-time customer identification for cashier-less retail
Retail · NDA
A retail partner wanted to strengthen in-store security and better understand customer behavior by integrating AI. We built a video intelligence system that detects and tracks movement, identifies repeated customers, and then processes and visualizes the collected data.
Real-time customer identification
Scalable tracking algorithm
Improved employee process management
Store layout optimization
Automated object measurement
Insurtech/ Construction · NDA
An existing object-measurement app lacked precision and had usability gaps across web, mobile, and admin interfaces. We rebuilt the architecture and developed a custom ML module for automated object detection and measurement, then added real-time collaboration and scalable AWS storage.
More precise automated measurement
Reduced manual errors
Web/mobile synchronization
Real-time collaboration
Automating unstructured customer requests
Sales operations · NDA
A sales team was manually processing unstructured incoming customer requests to fill in predefined templates. We built an AI workflow automation solution that extracted relevant information, converted it into structured data, and automatically populated templates, with post-processing to control output quality.
Less manual processing for the sales team
More consistent customer responses
Structured downstream data
Automated extraction
Why Aimprosoft as your generative AI integration company
Production AI experience
Measurable delivery
94%
sprint goal hit rate
81
client NPS
Engagement continuity
90%+
team continuity
72%
clients return for their next initiative
AI governance from Sprint 1
✓ Live delivery dashboard
✓ KPI tracking
✓ Security and access controls
✓ Red status escalation within 2 business days
FAQ
With the help of generative AI integration services, you can embed AI models into you systems so they become part of your business operations. Answering the question of what is AI integration in more technical terms, that’s safely connecting and orchestrating the model to the right data and business systems through APIs and workflow logic that follow security and business rules.
Our AI integration consulting services cover the full suite of work needed to take an AI use case from discovery and architecture through implementation and handoff. Depending on your needs, AI integration services can include system and data integration, RAG or agent workflows, security and access controls, testing, monitoring, and production deployment.
It depends on scope, but generally the average timelines for typical integration engagements are:
- Days to 2 weeks for discovery if the business process is already well-defined
- 2–6 weeks to design architecture and a working proof of concept
- 3–12 weeks for full AI implementation depending on the number of required integrations and compliance needs
As a generative AI integration company, we provide a concrete estimate after discovery.
Yes, that’s the whole point of enterprise AI integration services. We’ve worked with ERPs, CRMs, knowledge bases, custom applications, and legacy systems using APIs and integration layers to connect the dots. We plan the AI solution, taking into account your current architecture and business needs to embed it with minimum possible changes to your workflows.
AI consulting helps you assess potential use cases and define an overall strategy. AI integration takes care of the technical realization of your already scoped integration project. Most engagements need both, which is why AI integration consulting takes the solution from strategic decisions through delivery as one project.
It’s difficult to provide a concrete estimate, as the cost depends on numerous factors, including the number of integrations, data sources, workflow complexity, and compliance requirements. AI business solutions can range from $20,000 for a focused, single-system integration to $400,000 for a multi-system enterprise rollout, plus the ongoing costs for model infrastructure. We scope the project based on your actual stack and integration needs during discovery, and then provide an estimate.
You don’t need perfectly structured data to get started. What you need is to know where it’s stored, how consistent it is, and who can access it. Before we begin any enterprise AI integration work, we check whether information is centralized or not, whether it’s structured enough to work with, and whether there are gaps that would need cleanup first. Most companies figuring out how to incorporate AI into their business after assessing their AI readiness find their data is more ready than they assumed.
That’s one of our main generative AI integration services, which connects self-hosted or open-source LLMs to your CRM or custom-built tools via APIs or other proven integration methods. Access to data remains scoped to existing permissions and complies with your security policies.
Establishing AI governance for the solution is part of our integration process, which is addressed at the start of the project. After understanding your needs and specifics, we recommend and put in place appropriate security safeguards, required human oversight, and rules guiding AI interactions with your business workflows.