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 agents that carry out business tasks across your existing software
We help you define the workflow, connect the required systems, and prepare the agent for production with evaluation, monitoring, and human approval where needed.
Whether you are starting with a business problem or a working prototype, we scope the development around the outcome you need: fewer manual steps, faster processing, or a new capability inside your product.
Resolve routine requests using your knowledge base and product APIs. The agent can find invoices, check account status, resend invitations, or prepare a case for an operator.
Sensitive changes follow the approval rules agreed with your team.
Turn incoming document packs into structured information, completeness checks, and review tasks. The agent identifies missing or conflicting information and prepares clarification requests.
Specialists retain responsibility for final decisions.
Investigate payment and settlement exceptions using approved records and tools. The agent gathers evidence, explains discrepancies, and routes cases to the right owner.
Financial calculations and ledger controls remain in deterministic software.
Help employees find information in approved sources and take the next permitted step, such as opening a task or preparing a report.
Access follows the user's role; unresolved questions are escalated with supporting evidence.
Support lead qualification, CRM updates, and follow-up preparation. The workflow can combine business rules with contextual analysis.
Outbound communication can require review when your policy calls for it.
Tell us which process you want to improve and which systems it uses. We will review the scope and identify the next steps.
Discuss your use caseSee how our agent development engagements moved from a business problem to an integrated workflow, with clear boundaries and measurable acceptance criteria.
A B2B SaaS team had a working chatbot prototype, but support staff still had to check account records and complete routine requests manually.
We connected the agent to the help desk, knowledge base, and selected account APIs, and added role checks, permitted actions, operator handoff, and evaluation scenarios before a controlled rollout.
For a workspace access request, the agent checks the requester’s permissions, invitation status, and available seats, then resends an eligible invitation or creates a ticket with the supporting records.
We compared active handling time and repeat-contact rates for matched request categories during a four-week baseline and a four-week pilot, and independently reviewed a random sample for action correctness and permission compliance.
A finance team needed a faster way to investigate discrepancies between payment provider reports and internal transaction records.
We combined a deterministic reconciliation engine with an agent that gathers evidence through read-only APIs. The delivery included provider integrations, analyst review, audit records, and tests for duplicate events, partial refunds, and unavailable APIs.
When a payment is missing from a settlement report, the agent checks cutoff times, refunds, and subsequent reports. It prepares an evidence-backed explanation or routes the unresolved exception to an analyst. Ledger changes require a separate approval process.
We compared median analyst handling time by provider and exception type across a six-week baseline and a six-week pilot, and tracked explanations accepted without substantive correction, reopened cases, and backlog relative to incoming volume.
An onboarding team wanted to reduce the manual work needed to prepare company document packs for specialist review.
We connected document intake, OCR, a versioned requirements checklist, and the case management system, and added source references, discrepancy detection, reviewer approval, and evaluation of extraction quality.
The agent checks document completeness, highlights conflicting information, and drafts clarification requests. Reviewers approve external messages and retain responsibility for final onboarding decisions.
We compared preparation time across a four-week baseline and a four-week pilot, matching packs by language, document count, and scan quality, and independently checked critical fields for both missed discrepancies and false alarms.
Have a similar workflow? Let’s scope the development.
Discuss your projectStart with a scoped assessment, take an existing prototype to production, or bring us in for the full build and support after launch.
Discuss your projectWe map your process, data sources, decision points, and existing systems before you commit to the full build.
We build the agent's tool use, context handling, orchestration, and interfaces around your business task. The implementation is tested against representative cases and agreed limits on autonomous actions.
We connect approved tools and data sources to your systems. Integration work includes permissions, error handling, and recovery behavior.
We assess an existing prototype and address the gaps that prevent reliable deployment.
We build repeatable tests, prepare the release, and support improvements after launch. Changes to prompts, tools, retrieval, and models are checked against the evaluation set.
We recommend the simplest approach that meets your task and reliability requirements.
Steps are predictable, inputs are structured, and business rules are stable.
The task is retrieving information and answering questions from your documents.
The system must select and execute permitted actions as context changes.
Separate tasks or responsibilities benefit from distinct contexts and coordination.
Several integrations alone do not require multiple agents.
Your agent takes over the routine work. Your team keeps the decisions that matter.
A customer request, a new document, or a system event starts the workflow automatically.
No new tools for your team to learn
The agent retrieves only the context it is authorized to see and proposes the next action.
Sensitive data stays where it belongs
Access and business rules are checked on every action. Where approval is required, an authorized person decides.
Your team keeps the final say
Each result is verified. Unclear cases go to your team with source evidence, action history, and a short explanation.
Fewer manual steps, no silent failures
Your agent works inside the tools your team already uses. We connect it to your stack instead of asking you to replace it.
The exact scope depends on available APIs, data quality, and your permission model.
Read and write access are defined separately, with scoped credentials for each connected system.
We design for rate limits, timeouts, and unavailable dependencies from the start.
Where supported, idempotency keys and duplicate detection prevent repeated writes. Ambiguous outcomes get additional checks or human review.
You decide what the agent can see, do, and change, and you can stop it at any time.
Access is enforced in application code on every tool call, not left to the model.
Sensitive actions pause until an authorized person approves them. Prohibited actions are defined separately.
Tenant data is kept separate. Retention and logging follow the requirements agreed with you.
Documents and user messages are treated as data, with controls against instruction injection.
Every tool action is recorded, execution is capped, and the agent can be suspended at any time.
Controls are selected for your workflow and reviewed with your technical and security stakeholders.
Discuss your requirementsAcceptance criteria are agreed before the build, so your release decision is based on evidence, not a demo.
Did the workflow achieve the required outcome?
Were the right tools and parameters used within permissions?
Which cases need review, correction, or escalation?
Handling time, latency, and cost per completed task.
New workflows are compared against agreed acceptance tests. Every report states the measurement period, sample, method, and limitations.
Every stage ends with a clear deliverable, so you can decide on the next step with full information.
Define the business task, users, systems, success criteria, and limits.
You getWorkflow scope and acceptance plan
Validate data access and integrations, then test the approach on representative cases.
You getPrototype findings, risks, and a build recommendation
Implement agent logic, interfaces, tools, permissions, approval flows, and evaluation.
You getIntegrated solution ready for staging review
Phased rollout or pilot to assess performance in your target environment.
You getMonitoring, escalation paths, and an operational runbook
Review failures and usage, maintain integrations, and test changes before deployment.
You getAgreed support responsibilities and response times
A single workflow with a few well-documented integrations is a different project from a system spanning several departments or sensitive actions. We estimate after reviewing your data, APIs, required controls, and acceptance criteria, so the budget reflects your actual scope.
Priced separately, so you can validate the approach first
Build, integrations, controls, and evaluation
Model calls, hosting, retrieval and storage, monitoring, and support, reviewed against your task volume and model choices
Share your workflow and target date to receive a scoped budget and delivery plan.
10+years building blockchain-based financial projects, from crypto exchanges to payment gateways
We take responsibility for building and launching a scoped agent workflow end to end.
Our engineers work alongside your developers on the parts you want to strengthen.
Scope, ownership, communication, acceptance criteria, and support responsibilities are documented before work begins.
For fintech products, we assess the surrounding software too: transaction logic, API dependencies, data access, and operational recovery.
You see the implementation, evaluation results, and known limitations. We separate verified experience from proposed capabilities.
A successful development partnership depends on clear scope, sound engineering decisions, and visibility throughout delivery.
“We needed more than a convincing demo. The team helped us define which requests the agent could handle, connect it to our product, and test the actions before rollout. We had a clear view of what was ready and what still needed work.”
“The strongest part of Merehead’s approach was keeping financial calculations in our existing software while using the agent to investigate exceptions. The evidence links and review steps gave our analysts a practical way to check each conclusion.”
“The team focused on how our reviewers actually worked. Source references, clear exception handling, and approval before sending clarification requests made the workflow easier to assess. The handover also covered monitoring and ongoing responsibilities.”
Since 2018, Yuri has been consulting companies on strategic planning, entering international markets, and scaling technology businesses. Ask him about your case directly.
Talk to an expertYes, subject to an assessment of its task performance and architecture. We review integrations, permissions, evaluation, and operational gaps, then recommend what can be retained and what needs further development.
A description of the task, representative cases, relevant documents, and API specifications. We agree sandbox access and a technical contact first. Production permissions are requested only for approved functions.
The list depends on your risk policy. Sensitive financial operations, destructive changes, or consequential external messages may require approval. Routine actions can be automated within explicit rules and permissions.
We distinguish safe transient failures from ambiguous outcomes. Safe operations can be retried under defined limits. Other cases are checked or escalated, with recovery procedures agreed for the action type.
Only when separation of tasks or responsibilities justifies the added coordination and testing. A single agent or conventional automation may be easier to operate and sufficient for your needs.
Code ownership, evaluation assets, documentation, and usage rights are defined in the contract. Third-party software and model services remain subject to their own terms.
API changes, model updates, new workflow cases, and changes to source data can affect performance. Support can include monitoring, integration maintenance, evaluation updates, and controlled improvements.
Tell us about the process you want to improve. We will review the scope and identify the next steps.