AI Development Services
We build AI-powered products that automate workflows, improve decision-making, and create new revenue streams. Merehead delivers production-ready AI systems-from strategy and data pipelines to deployment, monitoring, and continuous improvement.
130+ projects
since 2015
blockchain expert
Services
AI Development Services
Our AI development services cover the full lifecycle: discovery, architecture, model development, integration, and MLOps. We focus on solutions that work reliably in real business environments, not just impressive demos.
01
AI Product Development
We take your AI product from idea to launch, including requirements, UX, engineering, and go-to-production readiness. You get a cohesive system designed for performance, safety, and maintainability.
02
LLM Application Development
We build LLM-based applications that handle support, sales, internal operations, and knowledge workflows. Solutions include RAG, tool/function calling, guardrails, and human-in-the-loop when needed.
03
Machine Learning Model Development
We develop ML models for prediction, classification, clustering, and anomaly detection based on your data and objectives. We prioritize measurable accuracy, robust evaluation, and stable performance over time.
04
Data Engineering for AI
We build data pipelines that make your AI reliable: clean inputs, consistent features, and governed access. This includes ingestion, transformation, quality checks, and scalable storage for analytics and training.
05
MLOps & Model Deployment
We deploy models with CI/CD, versioning, monitoring, and retraining workflows to keep performance stable. This ensures your AI remains accurate as data and user behavior evolve.
06
AI Integration into Existing Products
We integrate AI into your product via APIs, background jobs, and workflow automation with clear permissions and audit trails. This allows teams to ship AI features without disrupting core systems.
07
AI Consulting & Discovery Workshops
We run discovery workshops to validate use cases, data readiness, expected ROI, and technical feasibility. You leave with a clear roadmap, scope, and delivery plan.
Projects
What We Build
Step-by-Step
How Our AI Development Process Works
Our process reduces risk by validating feasibility early and shipping value in iterative milestones. Every step is designed to align data, models, and product experience with business outcomes.
Discovery & Use Case Validation
We define goals, KPIs, constraints, and success metrics to ensure the use case is worth building. This prevents misalignment and reduces time spent on low-ROI ideas.
Product Integration & UX
We integrate AI into your product with clear user flows, explainability cues, and safe fallbacks. The goal is adoption—users should trust and understand the AI output.
Model/LLM Build & Evaluation
We develop the model or LLM pipeline and evaluate it against real-world scenarios, not synthetic benchmarks. Testing includes edge cases, error analysis, and measurable performance targets.
MVP / Prototype
We build a prototype quickly to validate accuracy, UX, and workflow integration. Early results guide prioritization and confirm business value before scaling investment.
Data Audit & Feasibility
We assess data availability, quality, labeling needs, and privacy constraints. You receive a feasibility report with risks, mitigation steps, and recommended approach.
MLOps, Monitoring & Safety
We set up monitoring for accuracy, drift, latency, and cost, plus safety guardrails where needed. This keeps the system stable and prevents silent performance degradation.
Be prepared for the fact that there will always be a shortage of raw data for machine learning models. This is a serious problem to achieve maximum efficiency with the available dataset. On the other hand, integrating LLM models is very important, but it is important to control the token spending, as it increases the maintenance cost proportionally.
Industries
Industries We Serve
Intro
We build AI solutions for industries where data-driven automation creates measurable value. Our approach adapts to domain constraints such as compliance, security, and complex workflows.
SaaS & Enterprise Platforms
We add copilots, search, automation, and analytics features to existing platforms. Integrations are built for permissions, multi-tenancy, and scalable performance.
Fintech & Banking
We build AI for risk scoring, fraud detection, compliance automation, and customer support. Systems are designed with strong security controls and auditability.
Healthcare
We develop AI for document processing, triage support, and operational optimization where data governance is critical. Workflows prioritize privacy, safety, and controlled decision support.
Retail & E-commerce
We implement personalization, demand forecasting, dynamic pricing insights, and support automation. Solutions focus on conversion uplift and retention impact.
Logistics & Manufacturing
We build forecasting, anomaly detection, and vision-based quality control to reduce downtime and defects. AI is integrated into operations to improve planning and throughput.
Architecture
AI Architecture We Deliver
We design AI architecture that is secure, maintainable, and optimized for performance and cost at scale. The architecture ensures your AI system is observable, testable, and ready for continuous improvement.
01
Data Layer (Sources, Pipelines, Governance)
We connect data sources, build pipelines, and enforce governance to ensure reliable inputs. This includes access control, lineage, quality validation, and privacy-aware processing.
02
Model Layer (ML/LLM, RAG, Fine-tuning)
We implement the best-fit approach: classical ML, LLM-based RAG, fine-tuning, or hybrid pipelines. The focus is on accuracy, robustness, and predictable behavior under real usage.
03
Serving Layer (APIs, Latency, Cost Control)
We deploy models behind scalable APIs with caching, batching, and rate limits where needed. Serving is optimized for latency, throughput, and cost efficiency.
04
App Layer
We integrate AI features into your web/mobile product and internal tools with proper permissions and audit logs. Workflows are designed so users can validate, correct, and approve outcomes.
Security & Compliance Controls. We apply access controls, encryption, secrets management, and policy enforcement across the stack. Compliance controls ensure sensitive data and model outputs are handled responsibly.
Cost
Pricing and Timeline
Cost is driven less by “ML vs LLM” and more by data readiness + production constraints.
For ML, the biggest cost multipliers are whether you already have usable datasets (or must collect/clean/label them), how hard the problem is (tabular vs CV/time-series), and whether the model must work in real time with measurable accuracy and explainability.
For LLM projects, cost spikes when you move from a simple chat UI to RAG + evaluation + security: building a trustworthy knowledge pipeline, role-based access to sensitive docs, automated quality tests, and guardrails against hallucinations/jailbreaks.
For LLM projects, cost spikes when you move from a simple chat UI to RAG + evaluation + security: building a trustworthy knowledge pipeline, role-based access to sensitive docs, automated quality tests, and guardrails against hallucinations/jailbreaks.
The second major driver is integration and operations. Every external system (CRM/ERP/payments/data warehouse), every compliance requirement, and every reliability expectation adds engineering scope.
In practice, the “hidden” part of the budget is what turns a demo into a product: CI/CD, observability, cost controls (caching/routing), and ongoing iteration loops (retraining for ML, eval sets and prompt/model updates for LLM).
In practice, the “hidden” part of the budget is what turns a demo into a product: CI/CD, observability, cost controls (caching/routing), and ongoing iteration loops (retraining for ML, eval sets and prompt/model updates for LLM).
We have prepared a rough estimate of the cost of ready-made solutions with indicative price ranges for development. These are 'approximate' and can be adjusted depending on the requirements and specific needs of the client.
Our business development team will guide you through the legal and technical preparation that is essential when launching an AI product, drawing on our hands-on experience in developing machine learning models, LLM/RAG assistants, and production-grade AI workflows, so you start with the right architecture, clear requirements, and fewer costly surprises down the road.
Contact Expert
Who Should Launch AI Development
Startups building AI-first products
Enterprises modernizing workflows
SaaS companies adding AI features
Fintech businesses using analytics
Reason
Why Choose Us as Your AI Development Company
Merehead focuses on developing AI that works reliably in production and supports business growth. Our experience includes developing analytical systems for trading platforms using machine learning (ML) models. It is worth highlighting the experience of developing applications using LLM models that improve the user experience.
0+ years on the market
0+ completed projects
Thus, our experience helps to create stable solutions where budgets are usually exhausted due to improper planning and assessment of development complexity. A ready-made code base of key components based on Python, Next.js, NestJS, React, Go accelerates the development of standardized functions. This significantly increases quality and provides space for testing the final release.
The key advantages of our company are: extensive experience with ML, LLM and smooth integration into ready-made projects; availability of a code base to optimize development costs and time; an experienced team of project managers and business developers who help adapt your idea to market needs for further scaling your business.
The key advantages of our company are: extensive experience with ML, LLM and smooth integration into ready-made projects; availability of a code base to optimize development costs and time; an experienced team of project managers and business developers who help adapt your idea to market needs for further scaling your business.
Business-First AI Delivery
We start with KPIs, workflows, and ROI-not algorithms for their own sake. This keeps the build aligned with outcomes that matter to stakeholders.
Production-Ready Engineering
We deliver robust systems with monitoring, fallbacks, and operational readiness. The result is AI you can run and scale, not a one-off prototype.
Strong Data & MLOps Expertise
Data quality and MLOps determine whether AI works long-term, so we treat them as core components. This reduces maintenance cost and improves model stability over time.
Transparent Scope and Timeline
We define scope and milestones with clear acceptance criteria and measurable success metrics. You get predictable delivery and visibility across the entire project lifecycle.
One of the biggest challenges before starting AI development is choosing a team that can guarantee the quality and speed of development. Not all companies have practical experience integrating LLM and ML models into projects, which is a key advantage of Merehead among other similar companies.
FAQ
Have questions in mind?
Answers to the most frequently asked questions from our clients
Most MVPs can be delivered in 6–12 weeks depending on data readiness and integration scope. Production-grade systems typically require additional time for MLOps, monitoring, and security hardening.
Cost depends on the scope, data complexity, and reliability requirements in production. We estimate pricing by complexity and define milestones to keep budgets predictable.
Yes, we integrate with your databases, warehouses, CRMs, and internal tools while enforcing governance and access controls. If data is limited, we define a practical path for collection and improvement.
RAG is often the fastest way to deliver accurate, up-to-date answers using your internal knowledge base. Fine-tuning is useful when you need consistent style, specialized behavior, or domain-specific patterns at scale.
We implement privacy-aware data handling, role-based access control, logging, and audit-ready documentation. Safety controls and guardrails are included where the model interacts with users or sensitive systems.
Security
Responsible AI, Security, and Compliance
Data Privacy & Access Control
We enforce least-privilege access, encryption, and secure data handling across environments. This ensures sensitive business and customer data remains protected.
Model Safety, Guardrails & Policy Enforcemen
We add guardrails to prevent unsafe outputs, policy violations, and prompt injection abuse. Controls include validation layers, content rules, and safe fallback behaviors.
Bias, Evaluation & Human-in-the-Loop
We evaluate performance across relevant user groups and operational scenarios to reduce unexpected bias. Human-in-the-loop workflows provide oversight for high-stakes decisions.
Enterprise AI must be safe, auditable, and privacy-first from day one, because it touches sensitive data and real business decisions. At Merehead, we embed role-based access control, data minimization, encryption, audit logs, and model guardrails (policy filters, prompt-injection defenses, and human-in-the-loop approvals) so your AI stays compliant, predictable, and trustworthy in production.
Models
Engagement Models
Dedicated AI Team
A dedicated team works as an extension of your organization with consistent velocity and long-term ownership. This model fits multi-phase builds and ongoing optimization.
Fixed-Scope MVP
We deliver a defined MVP with clear outputs, timeline, and acceptance criteria. This is ideal for validating feasibility and ROI before scaling.
Team Augmentation
We add AI engineers, data engineers, or MLOps specialists to strengthen your existing team. This accelerates delivery without changing your internal product ownership.
Why is this important?
We offer flexible engagement models built around how AI projects actually succeed in production: clear scope, stable ownership, and measurable milestones. Each model includes a defined delivery rhythm (weekly demos, KPI-based acceptance criteria, and a shared backlog in Jira/Notion), plus engineering essentials like code ownership, documentation, and an agreed MLOps handover plan so progress is visible and outcomes are predictable.
