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
Insights resurfaced by conventional copilots
Deep insights that remain to be derived from data
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.
6
3-6 months of build.
Why General-Purpose AI Falls Short
See the difference with your own eyes
Routes questions to domain specific expert agents built on top of LLMs
Access enriched, proprietary, or industry-specific data for more accurate outputs
Can reason through workflows and logic tailored to sectors like education, healthcare, and finance
Built to deliver insights, not just answers - integrating agents that analyze, compare and act.
Continuously learns from vertical-specific use cases and feedback loops.
Others
Responds with a general model trained across broad datasets.
Limited to publicly trained data or whatβs available through prompts.
Lacks contextual memory and historical task recall unless manually engineered
Struggles with deep domain logic or workflows outside of standart prompts
Focuses on response generation rather than insight synthesis
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.