Services
Our company has been building blockchain-based financial projects for over 10 years. Our scope of activity includes the development of centralized and decentralized crypto exchanges, crypto bots, payment gateways, real estate tokenization, DeFi and NFT projects.
AI & ML
Since 2023 we have been building AI for business: custom AI and LLM development, AI agents, chatbots and enterprise AI that connect to your data, products and processes.
We build generative AI applications that help people find information, prepare documents, and complete work across business systems. From internal assistants to AI features for SaaS products, we handle development, integrations, testing, and deployment.
Bring us a defined project or a process you want to improve. We'll help you establish the scope, assess your data, and choose a practical starting point.
We review the workflow, available data, and expected result before committing to a full build. Discovery defines what the system should do, which actions require human approval, and how its performance will be evaluated. A focused prototype tests the assumptions that matter most to the investment.
We develop applications around your users and business processes. Our work covers the interface, backend, model integration, data access, and administration tools. The scope can range from an internal document assistant to a customer-facing product with accounts, subscriptions, and multiple user roles.
AI development servicesWe connect AI applications to company documents and databases through retrieval-augmented generation. The work includes preparing content, configuring search, preserving source references, and applying access rules. We also define how information is updated so the application can distinguish current guidance from outdated material.
We build copilots that assist employees and agents that perform defined tasks through approved tools. They can retrieve records, prepare responses, populate fields, and coordinate steps across applications. Permissions, review points, and exception handling are part of the workflow.
AI agent developmentWe assess whether the task needs better instructions, stronger retrieval, a different model, or fine-tuning. When fine-tuning is justified, we define the required dataset and evaluation process. The choice depends on the behavior you need and the evidence available to test it.
LLM developmentWe connect generative AI to existing products and operational systems. This includes authentication, APIs, background processing, monitoring, and deployment. After launch, support can cover errors, data-source changes, model updates, and improvements based on actual usage.
Answer routine questions using approved product information, policies, and support documentation. Connect authenticated requests to customer systems where appropriate, and hand conversations to employees when the available information is insufficient or the issue requires review.
Help employees search company information and prepare answers with references to the underlying sources. Access can follow team responsibilities and user permissions, so employees receive information relevant to their work.
Extract information from incoming files, identify missing fields, and prepare summaries or draft documents. Employees can review the source alongside the result before accepting it or sending it to the next stage.
Assist with work such as preparing customer replies, summarizing account activity, and drafting reports. The copilot brings together relevant information inside the tools employees already use, with review before consequential actions.
Add search, generation, document analysis, or conversational features to your product. We develop the supporting interface, permissions, usage controls, and backend services so the feature fits your application and commercial model.
We built an assistant for a carrier’s dispatch and invoicing workflow. It reads rate confirmations, gathers delivery documents, retrieves vehicle positions, and prepares records and messages for dispatcher approval.
During the four-week pilot, delivery-to-invoice time fell from three to five days to one day. The dispatch team recovered 22 hours per week. Outbound actions remained subject to human approval.
Read the trucking AI case study
We developed a decision-support POC combining six specialized LLM agents, numerical models, and vector memory. The system brings together market information and presents signals with supporting analysis through a dashboard and Telegram notifications.
The scope included decision history, evaluation tools, and paper-trading observation. Automated execution remained outside the initial scope.
Read the AI trading system case study
The application connects to the documents, records, and external sources needed for its task. We define which information belongs in a searchable knowledge base and which requests need current data from a business system.
We assign language generation, retrieval, calculations, and other operations to the appropriate components. This allows the team to evaluate the underlying result separately from the wording presented to the user.
APIs and background services let the application retrieve information and prepare or update records. Each integration needs defined permissions, validated inputs, and a response to failures.
We establish which outputs need review and which actions the system may perform independently. Review screens should show enough context for employees to approve, correct, or reject the result.
We build evaluation scenarios from the tasks the application will encounter. Testing covers incomplete information, conflicting sources, unexpected inputs, and integration failures. Acceptance criteria are agreed around the use case.
We define what each user and AI component may access. Sensitive information, tool permissions, credentials, and retention requirements influence the architecture and deployment choices.
The application needs a defined response when evidence is missing or a task falls outside its scope. Depending on the workflow, it can request clarification, show supporting sources, or route the work to an employee.
We monitor response times, failures, model usage, and operating costs. For a subscription product, the cost of completing a typical user task also informs usage limits and pricing.
We speak with the people doing the work, review representative inputs, and identify the steps the application will support. Together, we establish the scope, constraints, and success criteria.
We build a prototype to test the most important technical and product assumptions. This stage helps determine whether the available data and selected approach can produce a useful result.
We build the application, connect the required systems, and implement permissions and review workflows. Demonstrations give your team an opportunity to assess progress and resolve questions during development.
We test against the agreed scenarios, address issues, and prepare deployment and operational documentation. A staged rollout lets users review the system in a controlled setting before broader adoption.
After launch, we review errors, user corrections, source changes, and performance. These findings guide updates to retrieval, prompts, integrations, and application behavior.
The estimate depends on the workflow, data readiness, integrations, and requirements for deployment and review. We break the scope into deliverables so you can see what each stage includes.
| Project scope | Typical work included | Main factors affecting the estimate |
|---|---|---|
| Focused prototype | One core scenario, sample data, basic interface, evaluation | Input quality, model suitability, complexity of the task |
| Business application | User interface, permissions, integrations, testing, deployment | Number of workflows, system access, review requirements |
| Broader implementation | Multiple teams or processes, shared services, operational controls | Data ownership, integration dependencies, rollout and support needs |
We select the stack around your existing environment, data requirements, and expected workload.
We can discuss a defined project or ongoing development support. Team responsibilities, deliverables, and support arrangements are established in the proposal.
Discuss Your ProjectOur work includes the parts required to make an AI feature usable: interfaces, business logic, data access, integrations, and deployment. This is particularly useful when the application must work within an existing product.
We break projects into stages with defined outputs. Your team can assess the prototype, review integrations, and compare results with agreed criteria before expanding the scope.
Our projects involve documents, external systems, multiple user roles, and human review. We account for these dependencies when defining what the application can reliably do.
The initial engagement can focus on a specific workflow. Wider adoption, additional integrations, and new functions can follow once the first version has been evaluated.
“Merehead took the time to understand how our team handles insurance claims before building the AI agent. It helps us review incoming documents, find policy details, and prepare responses for our staff to check. We appreciated that the team asked practical questions early and worked through the exceptions with us. The agent fits into our daily work, and our people stay in control of the decisions.”
The cost depends on the scope, condition of the data, integrations, and deployment requirements. We prepare an estimate around specific deliverables and identify recurring expenses separately. If important assumptions remain untested, a focused discovery or prototype stage can help establish the full scope.
A narrow pilot and a business application have different requirements. Data cleanup, access approvals, and integrations can affect the schedule as much as development. We propose a timeline after reviewing these dependencies and the acceptance criteria.
Yes. We first review the application's architecture, available APIs, authentication, and data access. The AI functionality can be introduced through a separate service or integrated into existing components, depending on the product.
Many applications can use existing models with instructions and retrieval from company information. Fine-tuning is a separate decision. What we need initially is a representative set of tasks, relevant source material, and examples of acceptable results.
RAG provides relevant source information at the time of a request. Fine-tuning changes model behavior using training examples. We assess the problem first: missing knowledge, inconsistent output, and specialized behavior can require different solutions.
We establish which data the system uses, where it is processed, who may access it, and which external services receive it. Access controls, retention, logging, and deployment requirements are then reflected in the implementation.
Ownership, repository access, documentation, and handover terms are defined in the agreement. The proposal also identifies third-party services and software dependencies required to operate the application.
Yes. Support can cover monitoring, incident investigation, integration changes, model updates, and planned improvements. We agree on the scope and responsibilities before the support period begins.
Tell us what you want to build or which process needs improvement. Include the systems involved, the data available, and any requirements for timing or deployment.
We'll review the request and discuss the scope, technical options, and questions that need to be resolved before an estimate.
