🎉 Big News! Apta AI raised $2M in Pre Seed→
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🎉 Big News! Apta AI raised $2M in Pre Seed→
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Our technology

Automate complex workflows and access deep insights from advanced analytics, instantly. All through one conversational AI.
Adjectiveapt, suitable, fit (for), good (for), appropriate, adept
{Apt・a}
Agentic flow is the next paradigm for conversational AI
Large Language Models e.g. Llama, Mistral, ChatGPT excel in their language capabilities but are held back due their inability to handle diverse data types and to conduct multi-step reasoning.
Chip
Majority of conversational copilots (LLM-based) can only handle this
Unstructured Text DataNumericalLarge Structured DatabasesMultimodalCharts & Diagrams
Correct One
LLM Based
Hey, What's the weather today?
Correct Process:
Identify the user's location ⟶ cross-reference the live weather in that location  
Fetched your current location
weather24°C
London, UK
WeatherFriday 16:00Sunny
LLM Process:
Predicts the most likely next-word by modelling human language, without factoring in user location or live weather
Currently, Moses Lake, Washington, is experiencing clear skies with a temperature of around 41°F, though it feels closer to 36°F due to a slight northwest wind. The high today is expected to reach about 58°F with overcast skies throughout the day. The weather over the weekend will remain relatively cool, with highs around 60°F on Saturday and light rain expected on Sunday
In agentic flow, LLMs determine intent and formulate conversational outputs. Agents do the analytics and reasoning.
01 / Problem
LLMs are designed to predict the next word as they are probabilistic models that are not optimized to perform logic or rule based tasks. Generalist LLMs lack the functionality to capture deep insights from large scale data.
Problem
02 / Solution
We solve this by utilizing LLMs as the user facing mouth piece that then channel prompts to an array of bespoke expert models that are optimized for specific tasks.
Solution
03 / Our Edge
Our technology builds on PhD research from the University of Cambridge. We develop a structure of highly functional collaborative AI experts through sophisticated prompt routing, task decomposition, and advanced methods for efficient model interface. Apta leverages niche expert systems to develop highly insightful features tailored to specific domains.
Our Edge
Generalist agentic flow has limitations for specialised applications.
Large Language Models e.g. Llama, Mistral, ChatGPT excel in their language capabilities but are held back due their inability to handle diverse data types and to conduct multi-step reasoning.
“What are the attributes of US stocks that have performed the best in August 2024?”
Found Multiple Problems
Lack of access to specialized agents
Explainable ML Based agentic function to identify stock feature importance
Lack of access to specialized agents
Time-series price information for all stocks
Metadata for All Stocks
Latency for deep Insights
Generalist agentic flow has limitations for specialised applications.
Large Language Models e.g. Claude, Gemini, ChatGPT excel in their language capabilities and general tool use but are held back due their inability to handle highly complex workflows efficiently and accurately for bespoke industry applications.
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