AI strategy workshop
Gain clarity on your GenAI idea, business value, and implementation path from scope and goals to execution steps.
Launch generative AI products that improve customer experience, automate content, and speed up decision-making.
15-Minute Scoping CallGenAI development
Generative AI demos are easy to make; features that hold up with real users and real data are not. Agnotic Technologies builds GenAI features into your product, with evaluation and guardrails from the first release.
- Focused GenAI strategy and rapid implementation tracks
- Domain-ready assistants, copilots, and content systems
- Integration with your existing stack and business workflows
Trusted by global innovators
What is GenAI development?
GenAI development is building software features that use large language models or other generative models to draft, summarize, answer questions or extract data, connected to your own systems and content. Agnotic Technologies builds GenAI features for startups and enterprises, with retrieval, evaluation sets, guardrails and cost controls designed in from the first release.
GenAI solution development
Receive AI development from scratch or adaptation of existing solutions for your business tasks.
Generative AI integration
Integrate GenAI capabilities smoothly with your current infrastructure to enhance functionality and performance.
Generative AI consulting
Get expert guidance on planning and deploying generative AI solutions aligned with your business objectives.
GenAI features we build
Our GenAI development services cover the features below, each built into your product and tested against your own data.
Chatbots and virtual assistants
Enhance support and reduce operational cost by automating routine interactions with GenAI-powered assistants.
- Natural Language Generation for human-like replies
- Conversational analytics to optimize interactions
- Voice bots with multilingual capabilities
Intelligent document processing
Save time and reduce manual effort by automating document management with AI-powered extraction and understanding.
- Optical Character Recognition (OCR)
- Structured and unstructured data extraction
- Document understanding models (LayoutLM, Donut)
Synthetic data generation
Accelerate ML delivery when datasets are limited or sensitive by generating high-quality synthetic data.
- Audio and text data augmentation
- Neural style transfer for data diversification
- Adversarial training for improved generalization
Hyper personalization
Deliver individualized experiences at scale by tailoring content and recommendations for each user.
- AI-powered product and content recommendations
- Content generation based on real-time behavior
- User intent prediction for relevant engagement
Why Is GenAI Beneficial for Your Business?
GenAI development helps boost performance, reduce routine work, and create more advanced, personalized user experiences.
Automated workflows
Automate repetitive tasks to boost speed, reduce errors, and free your team for high-impact work.
Personalized experiences
Deliver AI-tailored content and interactions that match customer preferences and business goals.
Lower operational expenses
Reduce manual effort with AI-driven tools while maintaining service quality and reliability.
Streamlined development
Support developers with AI to speed up coding and improve productivity across delivery cycles.
Smart data insights
Turn complex datasets into insights that support faster decisions and stronger outcomes.
Smarter process automation
Automate complex workflows to remove delays, streamline tasks, and scale operations.
Should you prompt, retrieve or fine-tune a model?
Most generative AI features start by prompting a hosted model, add retrieval when answers must come from your documents, and fine-tune only when a format or style cannot be reached. Agnotic Technologies chooses the approach per feature, from your data and its processing rules.
Prompting a hosted model
Instructions and a few examples are sent to a provider's model through its API. It is the quickest way to find out whether a feature works at all.
- Fits when: the task needs general language skill, such as drafting, summarizing or classifying
- Data needed: a few good examples and a test set to score the outputs
- Updates: edit the prompt and re-run the tests, including when the provider ships a new model version
- Data question: does the provider's contract cover your data, and is it stored or used for training?
Retrieval-augmented generation (RAG)
The system searches your content first and passes the relevant passages to the model, so answers come from, and cite, your own documents.
- Fits when: answers must come from policies, manuals, records or other content that changes
- Data needed: the source documents, kept current, with access rights attached
- Updates: re-index the changed documents; the model is not retrained
- Data question: who may see each document, and does retrieval respect that for every user?
Fine-tuning
A model is trained further on your examples so it follows a format, tone or label set more consistently than prompting alone.
- Fits when: a narrow, repeated task needs consistent output that prompting cannot reach
- Data needed: reviewed input and output examples, cleaned of anything the model should not learn
- Updates: retrain and re-evaluate when the task or the base model changes
- Data question: training shapes the model itself, so may this data be used for training at all?
Self-hosted open-weight model
An openly licensed model runs in your own cloud account or data center, with prompting, retrieval or fine-tuning on top.
- Fits when: data must stay inside your environment, or per-call pricing does not suit your volume
- Data needed: the same as the approach you run on top of it
- Updates: someone upgrades the model, patches the serving stack and re-runs the evaluation
- Data question: which license terms apply, and who operates and secures the servers?
What does a generative AI application run on in production?
A generative AI application in production runs on six layers: model access, inference serving when self-hosted, a retrieval store, a gateway, evaluation and monitoring, and the network and keys around them. Agnotic Technologies designs this AI application infrastructure per feature.
Model access
How the application reaches a model: a provider's hosted API, a cloud AI platform in your account, or an open-weight model you run yourself.
- Decides: data terms, regions, model choice and cost per call
- Goes deeper: our cloud AI platform selection guide
Inference serving for self-hosted models
The layer a hosted API hides. A model server batches requests so the GPU stays busy, scales replicas on GPU capacity and queue depth rather than CPU load, and rolls out a new model version to a small share of traffic before the rest.
- Decides: response time under load, GPU spend and how safely a new model version ships
- Goes deeper: our GPU vs CPU inference comparison
Retrieval store
The index of your documents and records that retrieval-augmented features search before the model answers, kept current as sources change.
- Decides: answer grounding, freshness and whether access rights follow each user
- Goes deeper: our knowledge base and RAG development page
Gateway
One entry point between your applications and every model: API keys held centrally, routing between models, rate limits, and logs of prompts and responses.
- Decides: who can call which model, spend limits and the record kept of each call
- Goes deeper: our LLM guardrails page
Evaluation and monitoring
Test sets run before every prompt or model change, and production checks on quality, safety and cost after release.
- Decides: whether a change ships and how quickly a quality drop is noticed
- Goes deeper: our LLM evaluation page
Network and keys
Private network paths to model endpoints, encryption keys you control, and which services fall under your provider's BAA for health data.
- Decides: whether data leaves your network and which services may handle PHI
- Goes deeper: our cloud AI platform selection guide
Facts and sources
Which frameworks guide GenAI development?
Short facts we design, test and secure generative AI features around, each linked to its primary source.
The NIST AI Risk Management Framework is a voluntary framework that organizes AI risk work into four functions: Govern, Map, Measure and Manage.
Source: NIST AI 100-1In July 2024, NIST released the Generative AI Profile (NIST AI 600-1), a companion to the AI RMF that helps organizations identify risks specific to generative AI and proposes actions to manage them.
Source: NIST AI 600-1The OWASP Top 10 for LLM Applications (2025) ranks prompt injection first: user prompts that alter a model's behavior or output in unintended ways.
Source: OWASP GenAI Security Project, LLM01:2025The OWASP Top 10 for LLM Applications (2025) lists sensitive information disclosure (LLM02) as the risk that LLMs embedded in applications expose personal data, health records, confidential business data or credentials through their output.
Source: OWASP GenAI Security Project, LLM02:2025
What do buyers ask about GenAI development?
It depends on whether answers must come from your own documents, how quickly data access and permissions are settled, how strict the evaluation set is and how many systems the feature connects to. We give you a timeline after the scoping call.
Yes. GenAI features call your APIs, read your documents and databases through retrieval, and write results back to the apps your teams already use, with each model call logged through a gateway.
Yes. The first release covers one workflow, such as answering support questions from your help center or extracting fields from one document type, with an evaluation set agreed before the build.
Yes, as a feature we build for your QA team. GenAI can draft test cases from requirements and summarize failing test runs, and an engineer reviews every output before it is used.
AI tools help our engineers draft code and tests, but a person reviews every change, and it passes the same automated tests and security checks as any other code. Which tools may see your code or data is agreed with you at the start.
Related AI services
We Are Technology-Agnostic
With a diverse technology stack, we deliver solutions using a technology-Agnostic approach to meet your unique needs.
Wireframe & Ideation
User Experience
Real-Time Projects
Traditional Development vs. AI-Assisted Engineering
By adopting AI-assisted development, you can significantly reduce team size and development costs while maintaining the same speed, quality, and reliability of delivery.
Traditional development team
FTE
Business AnalystSolution ArchitectUI/UX DesignerProject ManagerBack-end EngineerFront-end EngineerQuality Assurance
- Manual coding and rule-based logic for each feature
- Full-time development team including all key roles
- Predefined workflows and human decision-making
AI-assisted engineering team
FTE
Solution ArchitectBack-end EngineerFront-end EngineerBusiness Analyst
Part time
UI/UX DesignerProject ManagerQuality Assurance
AI Agents
PlanningDesignCodeCI/CDTesting
- Orchestrating AI agents to automate coding and optimization
- Lean team using AI agents to expand development capacity
- Faster delivery with adaptive, data-driven improvement
AI Tools We Use for Software Engineering
We use AI-powered tools and automation frameworks to speed up every stage of the software development lifecycle.
Coding copilots
AI tools that assist engineers with code generation, refactoring, and documentation through contextual suggestions and inline reasoning.
AI agent frameworks
Multi-agent frameworks that enable coding assistants and context-aware workflow orchestration across CI/CD pipelines.
Data analysis and big data processing
Tools and systems for processing, analyzing, and transforming large unstructured datasets.
Infrastructure and orchestration platforms
Cloud and containerized environments that host, secure, and monitor Artificial Intelligence-assisted workflows.
Voice of success
We don't just build products; we forge lasting partnerships. See how we've helped industry leaders transform their vision into technical reality.
“I can clearly see how Agnotic has a unique way of handling end-to-end development. They are always active on quick chat and provide support quickly.”
Aaron Phelan
Founder, Benchmark