We Create Powerful Generative AI Models using Your Own Data
We bridge the gap between human potential and artificial intelligence capacities, taking your business to unforeseen heights.
Start Making Objective & Timely Decisions using Your own
Generative Pretrained LLM, "PRIVATELY"
Create Your Own Private GPT
We can develop your own private Genarative AI LLM within the confines of your establishment, under your private security setup, in whatever structure that suits you best, whether in-premise, private cloud, public cloud, or hybrid cloud.
Our developer-neutral setup allows us to replicate our clients’ business process into a machine learning model, starting with the END OUTPUT in mind and designing backwards. This way our clients see their employees adopt our solutions and tools easily and the learning curve is cut by almost 80%.
And to make matters even simpler, our clients end up using daily natural language when operating our tools. SIMPLE and very EFFECTIVE.
Create a Task-Based ML Model
We design Scalable Natural Language Models for our clients using pre-trained large language models that fit the specific business unit or task needed to be automated and powered by a GPT engine.
We collect relevant datasets from our clients stored data, whether about customers, suppliers, products, or even employees. Depending on their targeted goal or output, we will gather domain or sub-domain data in order to either Classify, Detect, or Predict persisting anomalies from within their datasets.
We create pipelines with such data after preprocessing them, we train the data using a larger but relevant LLM, then we produce an internally trained Large Language Model that our clients may use for all their processes.
Proof of Concept : Step-by-Step
1. Identify a Use Case where either there is pain or tremendous potential
2. Decide on preferred OUTCOME (Output/Goal)
3. Assign the Data Science method: Classification, Detection, or Prediction
4. Map the required Dataset (Features & Formats (Text, Audio, Video, Tabular)
5. Sign the NDA and start preprocessing the datasets & training them
6. Have the PoC ready for testing by the client locally or online (client's choice)
