Single Workflow / Process / Objective Focused on automating one clearly defined business process. Up to 10 Custom Tools Purpose-built integrations and functions to extend agent capability. Unlimited MCP Server Tools Leverage external cloud services securely via Model Context Protocol. Simple Retrieval-Augmented Generation (RAG) We chunk your content, vectorize them, store embeddings in a lightweight vector store, and expose a retrieval tool to the agent for contextual lookups (implemented as one custom tool). Custom Instructions & Architecture Tailored prompts, memory, orchestration, and tool design for your use case. Basic Chat-Based Interface Lightweight UI to interact with and test your agent directly. Human-in-the-Loop Confirmations Manual approval steps where human oversight is required. Two Iteration Cycles Structured refinement rounds during testing to improve outcomes. Findings Brief & Roadmap Clear documentation of results and recommendations for next steps

What’s Not (and comes with further development)

Multiple Workflows / Processes Scaling automation to cover multiple objectives simultaneously. Advanced Custom UI/UX Full product-grade interfaces beyond the prototype chat UI. Extensive Multi-Agent Orchestration Coordinating multiple agents with an orchestrator layer. Advanced Retrieval & Model Optimization Hybrid search, re-rankers, multi-vector stores, vector hygiene, RAG pipeline, domain-specific model fine-tuning, production scale cost optimization. Advanced Data Persistence Long-term memory and detailed audit trails. Scalability & Performance Hardening Optimization for production scale and high-availability deployment. Enterprise-Grade Integrations Deep connections into critical production systems at scale. Production Guardrails Robust safety, validation, and escalation mechanisms. Administration Tools Multi-user roles, permissions, and audit logs for governance. Production-Grade Observability & Monitoring Real-time logs, performance metrics, and dashboards. Auxiliary Product Features Dashboards, notifications, and other non-core product features

Can't get better value-for-price than us

Creators of LarAgent • Official Laravel & Vue Partners

Get a Working AI Agent For Your Business In Just 5 Weeks

Most businesses don’t know where to start with AI Agents. The hype is everywhere, but the path to real business value is unclear. That’s why we created a fixed-scope, $20k Proof of Concept Sprint: in just 5 weeks, we’ll design and build an agentic AI solution to automate your real business processes, test it in practice, and give you a clear path for transforming it into a standalone or an embedded product.

Powered by Laravel + LarAgent - the open-source framework we built and maintain for delivering AI Agent Development Services.

Book an Introductory Call See How It Works

Why AI Agents & Why POC Sprint?

How can AI help your business? A POC sprint provides a real use case in 5 weeks.

AI Agents are best at automating work that’s repetitive, error-prone, unstructured, or scattered across multiple tools. These processes are essential to keeping the business running - but they frustrate people and drain your team’s energy and focus from higher-value work. Agents can take them off your team’s plate, handling what classic rule-based automation can’t - whether it’s processing documents, triaging support tickets, or orchestrating data across tools.

But unlike traditional software, agents don’t follow fixed rules. They are non-deterministic - meaning you can’t predict every output in advance. So, the only way to know if they can handle a process is to test them in action. Our AI agent development services help with precisely that - 5 weeks to deliver POC and validate AI on real workflows before you scale.

Not sure where to start? Book an introductory call

What Exactly Can AI Agents Do For Your Business?

AI Agents are best where rule-based automation breaks down. A.k.a the tasks your team hates, but your business can’t run without. With our custom AI agent development, we help automate those tasks with AI agents.

Repetitive manual tasks → triaging support tickets (SaaS), reconciling invoices (Finance), updating patient records (Healthcare).

Unstructured input handling → parsing PDFs (Legal), summarizing intake forms (Healthcare), categorizing customer emails (E-commerce).

Cross-system workflows → syncing orders across ERP + CRM (E-commerce), updating HR + payroll (HR Tech), ticket + asset updates (IT Service).

Form processing → loan applications (Banking), insurance claims (Insurance), onboarding forms (SaaS).

Customer feedback handling → analyzing survey results (Hospitality), categorizing product reviews (Retail), extracting themes from NPS (SaaS).

Error-prone work → data entry into CRMs (Sales), validating claims (Insurance), compliance checks (Wealth Management).

Customer communication → drafting replies to FAQs (Retail), appointment reminders (Healthcare), basic IT helpdesk responses (Enterprise).

Document review & analysis → flagging risky contract clauses (Legal), reviewing KYC documents (Finance), screening resumes (Recruitment).

Operational workflows → managing returns (E-commerce), supplier onboarding (Logistics), expense approvals (Corporate Finance).

Front-line assistants → guiding patients through check-in (Healthcare), answering order questions (E-commerce), handling policy FAQs (Insurance).

The 5 Week POC Sprint

Unlike traditional software, you can’t predict exactly how an AI Agent will perform until it’s tested. That’s why our POC Sprint is structured as a 5-week program: discovery, build, and iteration. You end up with a working agent tested in practice - and a clear decision point for what comes next.

11
Discovery & Design (Week 1): we define the business objective, analyze the processes, and design the agent architecture - instructions, tools, memory, and orchestration. We also map integrations, including custom systems and MCP servers.
22
Build the POC (Weeks 2–3): we develop the agents using Laravel + LarAgent, implement up to 10 custom tools, and connect unlimited MCP tools. We provide a simple chat-based interface so you can interact with the agent right away.
33
Test & Refine (Weeks 4–5): agents don’t follow fixed rules, so testing is critical. We deploy the agents in a realistic setting, observe performance, and refine through at least 2 feedback cycles to reach acceptable results.
44
Outcome At The End of the Sprint: you’ll have working AI Agents tested on real processes, plus a findings brief that evaluates what instructions, prompts, tools, and configurations worked best (and what didn’t). You’ll also get a scaling roadmap showing how the POC can evolve into a production-ready system - either as a standalone product or embedded in your existing platform.

Get in touch

Dati Chkhikvishvili

Chief Business Officer

Connect on LinkedIn

BOOK A CALL

Engagement Models

We keep the POC Sprint simple - fixed scope, fixed price, fixed timeline. No moving parts. And once the Sprint succeeds, we switch to our tried-and-tested engagement models to scale your agent into a standalone product or embed it within your existing platforms. With our decade long experience of building enterprise-grade systems, we ensure your AI Agent has the stability, scalability, and reliability to succeed in production.

Scope & Deliverables

The POC Sprint gives you a working AI Agent tested on one real process - with clear limits. So you know exactly what’s included with our custom AI agent development service.

Single Workflow / Process / Objective

Focused on automating one clearly defined business process.
Up to 10 Custom Tools

Purpose-built integrations and functions to extend agent capability.
Unlimited MCP Server Tools

Leverage external cloud services securely via Model Context Protocol.
Simple Retrieval-Augmented Generation (RAG)

We chunk your content, vectorize them, store embeddings in a lightweight vector store, and expose a retrieval tool to the agent for contextual lookups (implemented as one custom tool).
Custom Instructions & Architecture

Tailored prompts, memory, orchestration, and tool design for your use case.
Basic Chat-Based Interface

Lightweight UI to interact with and test your agent directly.
Human-in-the-Loop Confirmations

Manual approval steps where human oversight is required.
Two Iteration Cycles

Structured refinement rounds during testing to improve outcomes.
Findings Brief & Roadmap

Clear documentation of results and recommendations for next steps

Multiple Workflows / Processes

Scaling automation to cover multiple objectives simultaneously.
Advanced Custom UI/UX

Full product-grade interfaces beyond the prototype chat UI.
Extensive Multi-Agent Orchestration

Coordinating multiple agents with an orchestrator layer.
Advanced Retrieval & Model Optimization

Hybrid search, re-rankers, multi-vector stores, vector hygiene, RAG pipeline, domain-specific model fine-tuning, production scale cost optimization.
Advanced Data Persistence

Long-term memory and detailed audit trails.
Scalability & Performance Hardening

Optimization for production scale and high-availability deployment.
Enterprise-Grade Integrations

Deep connections into critical production systems at scale.
Production Guardrails

Robust safety, validation, and escalation mechanisms.
Administration Tools

Multi-user roles, permissions, and audit logs for governance.
Production-Grade Observability & Monitoring

Real-time logs, performance metrics, and dashboards.
Auxiliary Product Features

Dashboards, notifications, and other non-core product features

Beyond the POC: Prototype to a Product

A Proof of Concept Sprint is just the beginning - it shows your processes can be automated with AI Agents. The next step is scaling that prototype into a production-ready system. With our expertise in AI agent development services and years of building SaaS, mobile, and enterprise platforms, Redberry can evolve your POC into a full-fledged product.

Custom Web App

Turn your AI agent into a full-featured web application with intuitive dashboards, user workflows, and management tools. Whether it’s an internal tool for your team or a customer-facing platform, Redberry has delivered 150+ custom web applications across finance, healthcare, and enterprise. We apply that same rigour to evolve your agent into a polished product.

Multi-Tenant SaaS Platform

If your AI agent has value across multiple teams or clients, we can scale it into a SaaS platform. Redberry has taken dozens of SaaS products from zero to launch and beyond - with multi-tenant architecture, subscription models, and enterprise integrations. We bring the same expertise to transform your POC into a SaaS business.

People Who Make It All Happen

Meet the minds behind our custom AI agent development projects.

Revaz Ghambarashvili

AI Lead

Revaz is AI Lead at Redberry and an open-source author with 8+ years in PHP and Laravel. He created LarAgent, a Laravel-native framework for building and managing AI agents.

Nika Jorjoliani

Head of Engineering

Nika is Engineering Director at Redberry, where he leads delivery across complex Laravel, Vue.js and React products for clients in fintech, wealth management, e-commerce, and SaaS.

Gaga Darsalia

CEO & Co-founder of Redberry

Gaga is CEO and co-founder of Redberry, which he started in 2014 and grew into one of Georgia's leading software companies and an official Laravel Premier Partner with 100+ in-house engineers.

Let’s build your AI agent

TALK TO AN AI EXPERT

Why Laravel

+ LarAgent

You don’t need to reinvent the wheel to build intelligent agents. Laravel already brings persistence, workflows, and integrations; LarAgent adds the missing piece: an open-source framework for building, orchestrating, and scaling AI Agents with Laravel’s familiar conventions. Together, they give you speed, structure, and reliability.

Laravel Handles the Heavy Lifting

Queues, jobs, and events make multi-step agent reasoning natural. Eloquent gives you persistence and memory out of the box. Middleware, auth, and APIs simplify tool integrations. Instead of gluing libraries together, you build on proven foundations.

LarAgent Brings the Agent Framework

LarAgent is Redberry’s open-source package that makes AI Agent development feel like Laravel. It supports tools, structured outputs, multi-agent orchestration, memory, and event hooks - all with an Eloquent-like syntax. 500+ stars and among the top Laravel packages of 2025, it’s community-adopted and production-ready.

Ready to Scale Beyond the POC

With Laravel’s ecosystem (Filament for admin, Cashier for billing, Horizon for jobs, Nightwatch for monitoring), your agent can evolve from prototype to production seamlessly. LarAgent is built to plug into that ecosystem, so scaling doesn’t require a full rebuild - just iteration.

Trusted by Enterprises Worldwide

Laravel is no longer just a community-driven framework. With backing from Accel and new products like Laravel Cloud and Nightwatch, it has matured into a thriving ecosystem offering battle-tested tools for scaling, observability, and DevOps. From early-stage MVPs to Fortune 500-scale platforms, Laravel delivers both startup agility and enterprise-grade reliability.

Why Choose Us For AI Agent Development Services

We’re one of only 13 Official Premier Laravel Partners and one of 8 Official Vue.js Partners worldwide. With 200+ people and 12+ years of experience, we’ve delivered complex systems across finance, wealth management, healthcare, automotive, e-commerce and more in 30+ countries. Delivered on time, within budget, and built to last.

Let's discuss your idea

Frequently Asked Question

When choosing an AI agent development company, focus on finding a partner who truly understands your business needs. The best companies will offer a proof-of-concept or pilot program, allowing you to quickly test their solution and create a prototype before making a major investment. We developed and actively use LarAgent for precisely that - not to reinvent the wheel every single time we build a new agent.

Beyond technical skills, prioritize companies that communicate clearly, provide transparent pricing, and offer comprehensive ongoing support. A good development partner will take time to understand your specific challenges and work collaboratively with your team from initial concept through deployment and maintenance.

Success is defined by whether the agent achieves the business objective you set at the start. We test the agent in a realistic setting, refine it through at least two feedback cycles, and deliver a findings brief with clear recommendations for scaling.

We connect through APIs by building custom tools that let the agent read from and write to your systems. If your vendor provides a Model Context Protocol (MCP) server, we can plug it in directly for secure access. This way the POC agent works with your real systems - without requiring heavy integrations.

We handle integrations end-to-end. Your team’s involvement is mainly to provide access and documentation (API docs, credentials, sandbox environments, API keys, etc.). If deeper context is needed, we’ll guide your engineers, but the heavy lifting is on us.

We can usually start Phase 2 within 2 weeks of POC completion. Depending on scope, we ramp with a dedicated team using either Time & Material or Milestone-based models.

We’re model-agnostic. Most POCs run on OpenAI, but we also work with Anthropic, Google Gemini, Groq, Azure OpenAI (for EU/UK compliance), and can connect to custom or private models if required.

Yes. If you need strict data residency or compliance (e.g. GDPR, HIPAA), we can configure the agent to run on your preferred vendor, including Azure-hosted or private deployments.

Yes, though they’re lightweight at POC stage. We use structured outputs, human-in-the-loop confirmations, and scoped tool access to keep behavior within safe boundaries. Full production guardrails come in Phase 2.

It depends on scope. Small-scale embedded agents may start around $60k–$80k. Full standalone SaaS products can run into the $150k–$250k+ range. We scope and estimate after the POC once outcomes are proven.

We document everything - code, architecture, tools, and integrations - and hand it over cleanly. Because the POC is built on Laravel + LarAgent, your Laravel developers can pick it up without a steep learning curve.