πŸŽ‰ Big News! Apta AI raised $2M in Pre Seedβ†’
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πŸŽ‰ Big News! Apta AI raised $2M in Pre Seedβ†’
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Big News!
Apta AI raised $2M in Pre Seed

Domain Specific AI Agents
Real Answers

Built on LLMs. Trained on your data. Designed for Accuracy.
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Read our Story
Best in class AI Agents that operate within a dynamic agentic network that delivers swift and accurate responses
All in one broad capabilities designed to increased profitability and reduced errors
Communicate through natural language chat with dynamic response generation
Drive more informed decisions through data informed quick expert responses
Dynamic Agentic Network Architecture is the future of AIDynamic Agentic Network Architecture is the future of AI
AI that can do more than LLMs
By harnessing the power of pre-emptive AI agents that are trained to do multiple complex business processes almost perfectly
Unstructured Text DataCharts & DiagramsNumericalLarge Structured DatabasesMultimodal
Majority of conversational copilots (LLM-based) can only handle this
Tailored AI Solutions
Copilots that derive deep insights.
Unlock the full value from your data through advanced analytics, rather than re-compiling existing knowledge
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Insights resurfaced by conventional copilots
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Deep insights that remain to be derived from data
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Capabilities
AI that can do complex tasks, accurately.
We use agents to execute complex multi-step workflows, so you can use our copilots for more than just text processing and coding
Large Language Models are good at
i.e. writing emails, summarizing documents, correcting grammar
Coding Workflows
Text WorkflowsSatisfied by web searchR.A.Gpython code generator and other agents
Text Workflows
Satisfied by web searchR.A.G
python code generator and other agentsLarge Language Models are good at
i.e. writing emails, summarizing documents, correcting grammar
Coding Workflows
To develop the best AI assistants, we adopt a different approach.
Generalist copilots i.e. GPT-4o try to cater to a broad range of use cases
We build copilots tailored for a particular vertical. That way we unlock a deep-set of AI capabilities.
Process Map
To develop the best AI Assistants
1
Provide high level overview of required scope.
2
Evaluate Apta AI's existing AI agents ready for fine tuning.
3
Determine additional agentic requirements necessary for scope.
4
Provide high level overview of required scope.
5
Patner with apta.
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3-6 months of build.
Why General-Purpose AI Falls Short
See the difference with your own eyes
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Routes questions to domain specific expert agents built on top of LLMs
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Access enriched, proprietary, or industry-specific data for more accurate outputs
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Can reason through workflows and logic tailored to sectors like education, healthcare, and finance
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Built to deliver insights, not just answers - integrating agents that analyze, compare and act.
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Continuously learns from vertical-specific use cases and feedback loops.
Others
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Responds with a general model trained across broad datasets.
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Limited to publicly trained data or what’s available through prompts.
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Lacks contextual memory and historical task recall unless manually engineered
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Struggles with deep domain logic or workflows outside of standart prompts
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Focuses on response generation rather than insight synthesis
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Broad feedback across unrelated use cases makes optimization harder.
About Us
Who are we?
Founded by a world-class team of PhDs from the University of Cambridge. We have collectively produced >30 AI papers in leading journals and conferences. Our team members have worked at some of the leading institutions in the space including Meta, Amazon Web Services and McKinsey. This wealth of experience places us at the forefront of AI innovation, driving us to deliver best-in-class AI solutions that redefine industry standards.
Some companies our team has worked with
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