The bot checks the contracts for possible risks from the point of view of a corporate lawyer and gives a detailed answer.
Technical Details: OpenAI/GPT-4 turbo API is used
Development period: 3 weeks
100% error reduction
Up to 80% budget savings on
The pilot project is ready for up to 6 days
It works 24/7
Increases employee efficiency by 10 times
We develop, implement and train robots
See how quickly you can build a schedule for the distribution of planned and actual deadlines for completing tasks by type of work for the last quarter.
That's how, in a couple of seconds, the robot can build a schedule of planned and completed tasks by Kirill Petrov.
The bot checks the contracts for possible risks from the point of view of a corporate lawyer and gives a detailed answer.
Technical Details: OpenAI/GPT-4 turbo API is used
Development period: 3 weeks
Automatically collects news from more than 50 sources. Excludes duplicates, finds the original source of the news. Next, the news is evaluated by importance in order to eventually offer a digest of only 25 of the most relevant news once a week. It is completed based on feedback from the user.
Development period: 4 weeks
The bot provides consultations based on the author’s content and in the style of a sexologist. It is impossible to distinguish a specific person from a living one.
Technical Details: The Mistral/Small API (Mixtral 8x7B) is used
Development period: 2 weeks
Prepares tests in the required format based on regulatory and corporate documents. There is also a validation function and checking for the relevance of already created tests.
Development period: 3 weeks
A bot with extensible functionality for checking essays in English, motivational essays, etc. Provides detailed feedback, evaluates according to the evaluation system established for this type of test.
Technical details: The OpenAI/GPT-3 API is used.5
Development period: 2 weeks
The service extracts information from the resume, structures it into a tabular form. It works with any resume structure.
Development period: 2 weeks
Uses an original approach based on oriental medicine.Conducts a patient survey and makes a diagnosis. Currently, work is underway to finalize the appointment of treatment.
Technical Details: OpenAI/GPT-4 turbo API is used
Development period: 3 weeks
An internal first-line support bot in a large company (several thousand employees). As a database for responses, 10 thousand+ documents are used, which were originally transmitted in MS Word, PDF, TXT, etc. format. They include several domains of knowledge.
Technical details: it works completely in the internal contour of the company on its own servers with GPU based on a pre-trained open source model
Development period: 3 months
The service selects the product according to the customer’s request. It can be used both for analyzing requests with a large number of products from the buyer, and for individual consultations, including using reviews from other consumers.
Development period: 2 months
In order to put the contract under control in automatic mode, the service extracts the obligations of the parties with deadlines and amounts, classifies (normalizes) by type of commitment.
Development period: 3 months
A complex solution aimed at increasing the level of control and implementation of software development projects. The service has access to corporate project management systems and evaluates:
Development period: 4 months
Sale of educational services and participation in conferences. A complex scenario of conducting a dialogue and guiding the client through the funnel has been implemented. The bot advises the client and answers questions about what external content is used for. Determines when the customer is ready to purchase the service and initiates the billing process.
Technical details: the Openapi/GPT-3.5 API was used, the ability to connect the bot via the API to Telegram and other interfaces.
Development period: 3 weeks
Security: data must be transferred to external servers, right?
Not necessary. We can deploy all the necessary infrastructure within the closed circuit of the customer’s company. For this we use advanced open source LLMs with licenses for commercial use. These models can be further trained by us using your data and to suit your tasks. At the same time, it is often more convenient and cost-effective to use third-party APIs from OpenAI, Mistral, GigaChat, etc. for insensitive data.
Do we need to hire additional expensive engineers to operate the systems?
We offer solutions based on microservices that can be seamlessly integrated into any systems and solutions via API. If, of course, this is provided for by the developers of basic solutions.
How can I integrate LLM into my company’s existing infrastructure and systems?
The time frame for developing basic solutions is indicated in the cases. It is quite difficult to predict in advance. But like a child, such solutions need to be nurtured, supported and transferred to a new technology stack for some time. Technologies are developing quickly and we provide for the possibility of a quick and painless change, for example, of a large language model by the customer. The education process takes a long time, but it’s worth it. Firstly, the solution begins to produce results from the first days of operation. Secondly, with each transaction it will become more complex and increase the competitiveness of your enterprise.
How much time and resources will it take to implement and adapt an LLM solution?
As a rule, we deliver all solutions in the form of docker containers, ready to be deployed with literally one command line command. This guarantees a simple and quick transfer, in case of need or restoration of functionality. Also, with each solution there are instructions for the user, administrator, description of the main modules and code with a sufficient number of comments for its understanding.
What support and documentation is provided with the LLM solution to ensure it works correctly and is used effectively?
As a rule, we deliver all solutions in the form of docker containers, ready to be deployed with literally one command line command. This guarantees a simple and quick transfer, in case of need or restoration of functionality. Also, with each solution there are instructions for the user, administrator, description of the main modules and code with a sufficient number of comments for its understanding.
What languages and dialects are supported by your LLM?
Depending on the chosen LLM, even rare languages can be supported. The database includes all popular languages. It is possible to train the model to better master specific terminology or dialect.
Can your LLM be customized and trained for specific tasks or industries?
Each solution is customized to the customer’s tasks and context of use.
How do you measure and improve the effectiveness of your LLM?
Most solutions provide feedback from users based on the results of interaction with the LLM. This allows us to constantly improve the results of its work. We also use various metrics and tests, as well as evaluating the model of another, independent LLM.
Can you provide a demo or test environment to evaluate your LLM?
Yes, sure. Most of our solutions have demo stands. If the solution is unique, then we test some of the hypotheses before concluding a contract and can demonstrate the results. Contact us for details.