Generative AI Development

Turn generative AI into a secure, production-ready capability built around your data, systems, and workflows.

Binariks develops and integrates generative AI solutions for healthcare, insurance, and financial organizations. We cover use-case validation, RAG, LLM customization, AI agents, enterprise integration, evaluation, deployment, and continuous improvement.

Get a Consultation

What Is Generative AI?

Generative AI is a category of artificial intelligence that creates new text, images, audio, code, and structured content based on patterns learned from data. Unlike traditional automation, it can interpret natural-language requests, work with unstructured information, and produce context-dependent outputs. Generative AI development turns this capability into usable software. It includes selecting appropriate models, connecting them with trusted data, integrating them into business workflows, evaluating output quality, defining security boundaries, and monitoring behavior after deployment.

Generative AI Consulting

Binariks' experts assess your workflows, data readiness, technical environment, and risk constraints to identify viable GenAI use cases. You receive a practical roadmap based on business value and implementation feasibility.

Custom LLM & GenAI Development

We develop domain-specific generative AI applications using suitable commercial or open-source models. Architecture, model configuration, prompts, data access, and controls are adapted to your operational requirements.

RAG & Enterprise Knowledge Systems

We build retrieval-augmented generation systems that connect language models with approved documents and databases, helping users receive contextual, source-grounded answers without relying solely on a model’s general knowledge.

Conversational AI & AI Agents

We create assistants and agents that answer questions, retrieve information, and complete controlled multi-step workflows. Permissions, tool access, approval rules, and human escalation are defined around each use case.

GenAI for Document Workflows

Apply generative AI to document-heavy processes such as summarization, information extraction, comparison, question answering, and draft generation, with validation logic and human review for sensitive workflows.

GenAI Integration & Support

We connect generative AI with your applications, APIs, knowledge bases, and cloud infrastructure. After deployment, we monitor quality, improve retrieval and prompts, and help the system adapt safely.

Why Clients Trust Us

Insights into our team achievements and valued partnerships

Net promoter score

200

Professionals in the team

150

Custom software solutions delivered

Years on the market

4.9

Client rating across platforms

The Expert Behind Our GenAI Practice

Mykhailo Hentosh

Head of Technology and Solutions

20+ years in software architecture and technology strategy

Core member of Binariks AI Center of Excellence

Leads AI solution architecture across healthcare, fintech, and insurance

Oversees production AI deployments from architecture to delivery

Specializes in secure, explainable AI for regulated industries

"Generative AI is most valuable when it doesn't just create more content, but helps people make better decisions and spend less time on work that never needed a human in the first place."

Talk to an Expert

Where Generative AI Creates Business Value

Generative AI is most useful when employees or customers need to find, understand, create, or act on large amounts of information. Binariks focuses implementation on workflows where value can be measured and human oversight can be clearly defined.

Faster Access to Knowledge

RAG-powered assistants help employees find relevant information across policies, clinical documents, claims files, financial records, and internal knowledge bases without manually searching multiple systems.

More Efficient Operations

Generative AI can summarize documents, prepare drafts, extract key details, and coordinate routine workflow steps, reducing repetitive effort while keeping sensitive or consequential decisions under human control.

Relevant Customer Experiences

Conversational assistants can provide contextual support, explain complex information, and guide users through defined processes while escalating exceptions to the appropriate employee.

Better Decision Support

GenAI can organize evidence, identify relevant context, and produce traceable summaries for review. Teams receive information faster without treating model-generated output as an unquestioned final decision.

Find the right GenAI use case

Let's assess your data, processes, risks, and expected value to identify where GenAI is suitable ??? and where a simpler technology may be the better choice.

Get a Consultation

Generative AI Across Regulated Industries

Healthcare GenAI must protect sensitive data, fit clinical workflows, and keep clinicians in control of decisions affecting patients. We build solutions that support care and operations without treating generated content as verified clinical judgment.

Generative AI solutions for healthcare:

Clinical documentation drafting and summarization

Patient communication and education content

EHR-connected knowledge assistants

Clinical trial document analysis and recruitment support

Medical and regulatory knowledge retrieval

Example:

Binariks built an agentic AI system for a US healthcare platform that automated clinical check-ins and documentation, reducing staff documentation time by 30% while maintaining auditability.

Explore healthcare AI case studies

Insurance organizations manage large volumes of claims, policy, underwriting, and customer documents. GenAI can help teams interpret this information faster while preserving access controls, evidence, and human approval.

Generative AI solutions for insurance:

Claims document analysis and summarization

Underwriting submission review support

Policy and coverage knowledge assistants

Customer-service and claims-status assistants

Regulatory and operational document workflows

Example: Binariks built a RAG-powered AI agent for a global commercial insurer, reducing risk-insight extraction time by 90% and manual review cycles by 80–90%.

Explore insurance AI case studies

Financial GenAI must work with sensitive information, preserve evidence, and support reviewable workflows. We focus on controlled document, knowledge, reporting, and customer-service applications.

Generative AI solutions for financial services:

Financial document extraction and summarization

Regulatory and management report drafting

KYC and AML investigation support

Internal policy and procedure assistants

Contextual customer-service automation

Example:

Binariks developed an AI-powered fund administration system that reduced report validation time by 90% and errors by 75%.

Explore fintech AI case studies

Healthcare and Life Sciences

Generative AI solutions for healthcare:

Clinical documentation drafting and summarization

Patient communication and education content

EHR-connected knowledge assistants

Clinical trial document analysis and recruitment support

Medical and regulatory knowledge retrieval

Example:

Binariks built an agentic AI system for a US healthcare platform that automated clinical check-ins and documentation, reducing staff documentation time by 30% while maintaining auditability.

Explore healthcare AI case studies

Insurance

Generative AI solutions for insurance:

Claims document analysis and summarization

Underwriting submission review support

Policy and coverage knowledge assistants

Customer-service and claims-status assistants

Regulatory and operational document workflows

Example: Binariks built a RAG-powered AI agent for a global commercial insurer, reducing risk-insight extraction time by 90% and manual review cycles by 80–90%.

Explore insurance AI case studies

Fintech

Financial GenAI must work with sensitive information, preserve evidence, and support reviewable workflows. We focus on controlled document, knowledge, reporting, and customer-service applications.

Generative AI solutions for financial services:

Financial document extraction and summarization

Regulatory and management report drafting

KYC and AML investigation support

Internal policy and procedure assistants

Contextual customer-service automation

Example:

Binariks developed an AI-powered fund administration system that reduced report validation time by 90% and errors by 75%.

Explore fintech AI case studies

Generative AI Development Deliverables

Every engagement produces practical strategy, engineering, evaluation, and operational assets. The exact deliverables depend on the use case, data environment, integrations, and level of risk.

Strategy & Solution Blueprint

We define what the system should achieve, where GenAI fits, and how success and risk will be measured before full-scale development begins.

Prioritized use cases and feasibility findings
Requirements, architecture, and integration plan
Success metrics, risks, and delivery roadmap

Data & Knowledge Foundation

We prepare the information the system will use and establish how models can retrieve it without crossing access, privacy, or governance boundaries.

Data and knowledge-source assessment
RAG, indexing, and retrieval architecture
Access, lineage, and data-quality rules

Production GenAI Solution

We build and integrate the selected model workflows, supporting components, and controls required to operate the solution in your environment.

Configured LLM, RAG, or agent workflows
APIs and enterprise system integrations
Evaluation suite, guardrails, and security controls

Operations & Ownership

We prepare your technical and operational teams to monitor, maintain, and improve the solution after launch.

Monitoring dashboards and operational runbooks
Architecture, model, and API documentation
Knowledge transfer and optimization backlog

Clients Across the Globe

Our Generative AI Development Process

Binariks moves from business validation and data assessment to architecture, implementation, evaluation, and production support. Each phase produces documented outputs so stakeholders can review progress and risk before further investment.

Step 01

Use-Case Discovery

We examine the workflow, users, expected value, decision impact, and current alternatives before determining whether generative AI is suitable.

Outcome: Prioritized use cases, feasibility assessment, business requirements, and success criteria.

Step 02

Data & Risk Assessment

We evaluate knowledge sources, data quality, access permissions, privacy constraints, and the consequences of inaccurate or inappropriate output.

Outcome: Data-readiness findings, risk register, governance requirements, and remediation plan.

Step 03

Architecture & Model Selection

We compare models, RAG, fine-tuning, agent tooling, hosting options, and integration patterns against quality, security, latency, cost, and maintainability requirements.

Outcome: Solution architecture, model rationale, integration map, evaluation plan, and delivery scope.

Step 04

Prototype & GenAI Development

We build a focused prototype, test the core workflow with representative data, and refine retrieval, prompts, tools, and user interaction before scaling.

Outcome: Working prototype, initial evaluation results, validated assumptions, and refined backlog.

Step 05

Evaluation, Integration & Deployment

We connect the solution with approved systems and test output quality, security, failure scenarios, permissions, escalation rules, performance, and user workflows.

Outcome: Integrated production solution, evaluation report, security configuration, deployment runbooks, and release approval.

Step 06

Monitoring & Continuous Improvement

After launch, we track output quality, retrieval performance, latency, cost, user feedback, and unexpected behavior while updating the system as data and requirements evolve.

Outcome: Monitoring dashboards, optimization roadmap, support model, and knowledge transfer.

Step 01

Use-Case Discovery

We examine the workflow, users, expected value, decision impact, and current alternatives before determining whether generative AI is suitable.

Outcome: Prioritized use cases, feasibility assessment, business requirements, and success criteria.

Step 02

Data & Risk Assessment

We evaluate knowledge sources, data quality, access permissions, privacy constraints, and the consequences of inaccurate or inappropriate output.

Outcome: Data-readiness findings, risk register, governance requirements, and remediation plan.

Step 03

Architecture & Model Selection

We compare models, RAG, fine-tuning, agent tooling, hosting options, and integration patterns against quality, security, latency, cost, and maintainability requirements.

Outcome: Solution architecture, model rationale, integration map, evaluation plan, and delivery scope.

Step 04

Prototype & GenAI Development

We build a focused prototype, test the core workflow with representative data, and refine retrieval, prompts, tools, and user interaction before scaling.

Outcome: Working prototype, initial evaluation results, validated assumptions, and refined backlog.

Step 05

Evaluation, Integration & Deployment

We connect the solution with approved systems and test output quality, security, failure scenarios, permissions, escalation rules, performance, and user workflows.

Outcome: Integrated production solution, evaluation report, security configuration, deployment runbooks, and release approval.

Step 06

Monitoring & Continuous Improvement

After launch, we track output quality, retrieval performance, latency, cost, user feedback, and unexpected behavior while updating the system as data and requirements evolve.

Outcome: Monitoring dashboards, optimization roadmap, support model, and knowledge transfer.

Real-World Success Stories

From concept to production ??? real AI outcomes our clients achieved in healthcare, fintech, and insurance

Healthcare

Streamlining hospital workflows: Agent-based automation of for clinical check-ins and documentation

30% less time

30% time saved on follow-up documentation tasks

View case study

Fintech

AI-powered fund administration for a global asset manager

75% fewer validation errors

View case study

Insurance

AI-powered claims analysis for a global insurance provider

90% faster risk-insight extraction

View case study

View All Case Studies

Trusted Clients

What our clients say about working with Binariks

Ioannis ChronakisCEO, Deontics

I'm impressed by the skill and enthusiasm of the people working on this project.
The primary measurable outcome is the successful delivery of components and assigned tasks for the completed phases of the project.

5.0

Steven ChristodoulopoulosSenior Director of eHealth Innovations & IT, Capsana

They have a lot of knowledge. We’ve built our data warehouse, and they have laid the foundation for our data analytics strategy. The solutions are performing as expected and meeting our business objectives.

5.0

Jake HalpertCEO, Healthcare Company

Binariks’ biggest strength is the quality of their people. We have a strong team leader who’s great at communicating with everybody and figuring things out quickly and smartly without getting too flustered or panicky. Overall, we have a great team structure with the right people and personalities.

5.0

Eugene BorukhovichCOO & Co-Founder, YourCoach Health

We highly value their customer-first approach. If we have something going on, they’re easily available. Our clients are happy with their work.

5.0

Takeshi SasakiFounder and CEO, Pivool Pte. Ltd.

The team at Binariks is vibrant, proactive, and efficient. Their autonomous nature complements their open approach.

5.0

Daniel CuendeCEO, CUENDE Infometrics

The attitude towards work is something Binariks takes pride in. It's a combination of a high-skilled team and quite mature management that makes this company stand out. They have a high level of responsibility and technical skills.

5.0

Amit PatankarCTO, BlueVoice Inc.

Binariks was capable of taking a design, providing adequate timelines and resources, scoping the project out, and delivering it. In my experience, most people can't deliver, but Binariks delivered.

5.0

XiaoChen GuoHead of Engineering, Planet of Finance

I appreciate how well they handle complaints. Developers don't always understand each other, and we're all working remotely. But Binariks has remained professional and humble. They always listen to what we want them to improve.

5.0

Tony LeeSenior QA Engineer, Modo Labs

I love the people I interact with at Binariks. [...] They're professional and down to earth, very easy to talk with. The QA resources I've worked with are very motivated and always want to get ahead of the game. If improvements are needed, they get done in a timely manner.

5.0

Russell HertzbergCTO, Briotix Health

Binariks' health technology expertise and their experience in building successful healthcare software solutions are their key differentiators. Additionally, they're extremely strong in having a complete, thorough, and high-quality architecture discovery phase.

5.0

John StruttonHead of Product Management, axitech

Binariks pays attention to the details. They take a high level of accountability and responsibility for tasks assigned to them. We can rely on them as part of our team rather than an external agency just putting in hours.

5.0

Michael MagdongonCEO & Co-Founder, Strabo

Binariks' clear communication is the best – they're consistent with it and with deliverables. They log their hours accurately, and if there's an issue, they respond in a timely manner. The team also sends meeting minutes after every management call, and their invoices come like clockwork.

5.0

Stuart FyfeManaging Director, Mothership Tech

Their communication has been spot-on. They've been transparent about how much time they spend on each sprint as well.

5.0

Dan EtheringtonHead of Software Development, Pelican Procurement

Binariks brings real value to our business with their contributions, and their positive and "can-do" attitude.

5.0

Gary LearnerCEO, Opus Digitas

They're agile and adaptable to change. They ask questions and don't make assumptions in order to understand your business and create the best possible solution. We've worked with lots of other partners, and they're one of the most organized and responsive.

5.0

Demian BigelowIntegrated Biometrics

What I really appreciate about Binariks is their mature technical expertise and high level of responsibility on given commitments: each expected increment was delivered as it was planned and just on time.

5.0

Sonya BeredimasVP Sales & Marketing, iVoyant

Their attitude towards their own work and their clients was impressive. They didn’t try to add unnecessary people to a project, it was a very lean and mature team.

5.0

Steve AnastasiCTO, EatStreet

They take a sense of ownership in these projects. Their team's focused and engaged in the work that they do. They're available all of the time and seem to be part of our team. Their team works well with our team's operation.

5.0

Damien JulienCreative Director, distylerie gmbh

We like Binariks' consulting. When we have an issue, Binariks always provides the right solution.

5.0

Victor CoutinFounder, Aire Fresco

They put a lot of emphasis on best practices for material design, quality user experience, and implementation. That’s going to be a key factor on customer retention.

5.0

Frederick CriminsFounder, Hamstoo

Their communication skills are excellent. The project manager is particularly great to work with as he’s pushed me to communicate with them more.

5.0

View All Testimonials

Generative AI Built for Real Operations

Binariks' generative AI development services turn language models into secure, integrated software for healthcare, financial services, and insurance organizations. We build RAG systems, knowledge assistants, AI agents, document-processing workflows, and conversational applications around the client???s data, users, infrastructure, and operational requirements.

Each solution is designed for production use rather than an isolated demonstration. That means connecting models with approved information, evaluating outputs against defined criteria, controlling access and actions, integrating human review, and documenting how the system operates. Clients receive a maintainable GenAI capability their teams can monitor and improve as business needs change.

Use cases validated before major investment
Models grounded in governed enterprise data
Secure integration with existing systems
Evaluation, guardrails, and human oversight
Traceable outputs and auditable activity
Documentation and knowledge transfer for ownership

Move from experimentation to an operational GenAI system

Bring us the workflow, data constraints, integration requirements, and risks. We'll help you define a production path that your users, technical teams, and compliance stakeholders can evaluate.

Ross Chornyy

Senior VP

+1 (929) 214-16-90 [email protected]

Schedule a Call

Frequently Asked Questions

What do generative AI development services include?

They can include consulting, use-case validation, data assessment, RAG, custom LLM applications, AI agents, document processing, model evaluation, enterprise integration, deployment, monitoring, and support.

How do we identify the right GenAI use case?

We assess workflow volume, information complexity, data availability, expected value, failure consequences, and existing alternatives. Some problems are better solved with conventional automation, search, analytics, or machine learning.

What is the difference between RAG and fine-tuning?

RAG retrieves relevant information when a request is made and provides it to the model as context. Fine-tuning changes model behavior using training examples. The approaches solve different problems and can be combined.

How do you reduce hallucinations and inaccurate outputs?

We combine grounded retrieval, prompt and workflow constraints, output validation, use-case-specific evaluation, source references, monitoring, and human review. No responsible implementation should promise that hallucinations can be eliminated completely.

How do you protect sensitive data?

Architecture may include private networking, encryption, role-based access, data minimization, approved model endpoints, retention controls, secrets management, audit logs, and restrictions on how providers may use submitted data.

Can GenAI support regulated workflows?

Yes, but requirements depend on the use case and jurisdiction. We work with client compliance and legal stakeholders to map controls, documentation, transparency, human oversight, and validation requirements into the solution.

Ask us anything

Let's Start Your Project

We'd love to hear about the project you're working on. Simply complete the form and we'll be in touch.

What happens next?

Our expert will reach out to understand your goals and challenges

If needed, we'll sign an NDA to ensure full confidentiality

You'll receive a tailored roadmap with solution suggestions, timelines, and budget estimates

+1 (929) 214-16-90 +(44) 748-881-7998 [email protected]