Domain-grounded assistants
Assistants and copilots grounded in your knowledge base answer from approved content, so responses reflect your business rather than generic model output.
Launch practical generative AI solutions to accelerate content, automation, and customer experiences.
15-Minute Scoping CallGenerative AI services for scalable product innovation
From copilots and assistants to media and workflow automation, we build generative AI solutions aligned to your business goals.
- Fast experimentation and controlled rollout of GenAI capabilities
- Domain-grounded assistants and content systems
- Smooth integration with existing products and operations
Trusted by global innovators
What is generative AI?
Generative AI is a class of AI models that create new text, code, images or audio from a prompt, based on patterns learned from training data. Agnotic Technologies builds generative AI services into your products and workflows: assistants grounded in your content, document extraction and drafting tools, with evaluation and guardrails designed in.
Content and media acceleration
Draft, summarize, and transform text, code, and media with a human in the loop, cutting turnaround time on repetitive production work.
Faster, safer experimentation
Start with contained experiments to find where GenAI genuinely helps, then promote only the capabilities that prove out to production.
Fits your existing products
We integrate generative capabilities into the products and operations you already run instead of standing up a disconnected tool.
Which generative AI solutions do we build?
Generative capabilities grounded in your data and integrated into real products, not standalone demos, so they earn their place in day-to-day work.
Copilots & assistants
In-product assistants that help users and staff complete tasks faster, grounded in your content and permissions.
RAG knowledge systems
Retrieval-augmented systems that answer from your documents and data, with citations and access controls.
Content generation
Drafting, summarization, and transformation of text, code, and media with a human-in-the-loop review step.
Document & data extraction
Turn unstructured documents into structured, validated data that flows into your downstream systems.
Model integration & tuning
Selection, prompting, and where warranted fine-tuning to fit models to your domain, quality bar, and cost targets.
Safety, evaluation & guardrails
Evaluation harnesses, content safety, and guardrails so generative features stay accurate, on-brand, and compliant.
Ship a generative AI capability that fits your product
From grounded assistants to content and extraction pipelines, we build generative AI into the products you already run. Tell us the use case.
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Partnerships
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Facts and sources
Which frameworks guide generative AI builds?
Short facts we design and test generative AI 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, LLM01:2025
What do buyers ask about generative AI?
It depends on how much of your content the model must draw on, how clean that content is, which systems the feature reads from or writes to, and how answers are tested before release. We start with one use case and give you a timeline after the scoping call.
Engineering effort depends on the number of data sources behind retrieval, the integrations, the evaluation set and the guardrails the use case needs. Running cost depends on the model you choose and how often it is called, which we estimate with you before launch.
Yes. We connect the model to your documents, databases and APIs through retrieval and tool calls, and place the feature inside the product or internal tool your team already uses, with access limited to the data each user may see.
Yes. The first release covers one use case, such as a document assistant for one team, with retrieval, guardrails and an evaluation set in place. More use cases follow once it runs well.
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