Most businesses are not short of repetitive, logic-heavy work that consumes skilled people's time. AI agents change that equation. Perimattic designs and builds production-grade AI agents and multi-agent systems that operate reliably in live environments, not just in demos.
Agent Frameworks & Foundation Models We Build On — LangGraph, CrewAI, AutoGen, LangChain, OpenAI, Anthropic Claude
An AI agent is a software system that perceives its environment, reasons through a goal, and takes a sequence of actions to complete it without requiring step-by-step human direction. Unlike traditional automation tools that follow fixed scripts, or chatbots that respond to single prompts, AI agents plan ahead, call external tools (APIs, databases, code interpreters), handle exceptions, and adapt their approach based on what they find along the way.
The practical implication for business is significant. An AI agent handling a supplier invoice does not just extract data. It queries your ERP, checks approval thresholds, routes to the right person, logs the action, and flags anomalies, all autonomously. That is the difference between point automation and genuine agentic intelligence.
Multi-agent systems extend this further: multiple specialised agents collaborate, delegate sub-tasks to each other, and complete workflows that span departments, systems, and data sources. Perimattic builds both single-agent and multi-agent architectures, designed from day one for production scale.
Decision approach
Follows a fixed script
Decision approach
Plans and reasons dynamically
Task scope
Single-turn or single-action response
Task scope
Multi-step task completion
Tool use
No tool use outside predefined integrations
Tool use
Calls APIs, databases, and code
Exception handling
Breaks or escalates on exceptions
Exception handling
Adapts to edge cases from context
Maintenance
Requires reprogramming for every new variation
Maintenance
Adapts from context, minimal reprogramming
The distinction matters most in workflows with variability: supplier communications, customer queries, clinical documentation, financial reconciliation. These are exactly where AI agents deliver measurable productivity gains.
Seven specialist service lines, each built for a specific type of agentic problem.
We map your workflows, identify the highest-value automation candidates, and design an agent architecture that fits your existing systems. Our AI consulting services start with strategy, not code.
We build agents from scratch to your exact requirements: tool definitions, memory configurations, reasoning loops, guardrails, and integration points. Every agent is built for production from the first sprint.
We design orchestrator-specialist architectures where a lead agent delegates tasks to a fleet of specialist agents, each with its own tools and scope. The result is a system that handles complex, multi-domain workflows reliably.
We build copilot assistants that augment your team rather than replace them. Copilots surface context, draft responses, and execute repetitive sub-tasks within tools your team already uses, from CRMs to ERPs.
We automate end-to-end workflows that span multiple systems and require judgement at each step. Finance reconciliation, supply chain co-ordination, onboarding sequences, and clinical documentation are common starting points.
We connect agents to your existing infrastructure: ERPNext, Salesforce, HubSpot, Slack, custom REST APIs, SQL and vector databases. We have deep ERPNext integration expertise and can wire agents into your ERP data in a single engagement.
We instrument agents with observability tooling so you can see what decisions they make, where they fail, and how to improve them over time. Includes LLM call tracing, latency monitoring, and accuracy evaluation frameworks.
A structured six-stage process from free scoping call to live deployment and ongoing optimisation.
We map your current workflows, identify where AI agents will deliver the fastest return, and define the scope of the first build. This session is free and carries no obligation. You leave with a clear picture of what to build and why.
We design the agent architecture: reasoning loop, tool registry, memory strategy, integration points, and guardrails. We select the right framework for your use case rather than defaulting to a single stack.
We build a working prototype against a real slice of your data and workflows. The PoC demonstrates the agent's core behaviour and surfaces any integration or data quality issues before the production build begins.
We build and integrate the full production agent, connecting it to your ERP, CRM, databases, and APIs. Security controls, authentication, rate limiting, and fallback logic are included from the start.
We test the agent against real production scenarios, including adversarial inputs and edge cases. We establish evaluation metrics and baselines so you have objective criteria for measuring performance.
We deploy the agent to your infrastructure and hand over instrumented observability. We monitor performance in the first weeks, resolve any production issues, and help you plan the next iteration.
We map your current workflows, identify where AI agents will deliver the fastest return, and define the scope of the first build. This session is free and carries no obligation. You leave with a clear picture of what to build and why.
We design the agent architecture: reasoning loop, tool registry, memory strategy, integration points, and guardrails. We select the right framework for your use case rather than defaulting to a single stack.
We build a working prototype against a real slice of your data and workflows. The PoC demonstrates the agent's core behaviour and surfaces any integration or data quality issues before the production build begins.
We build and integrate the full production agent, connecting it to your ERP, CRM, databases, and APIs. Security controls, authentication, rate limiting, and fallback logic are included from the start.
We test the agent against real production scenarios, including adversarial inputs and edge cases. We establish evaluation metrics and baselines so you have objective criteria for measuring performance.
We deploy the agent to your infrastructure and hand over instrumented observability. We monitor performance in the first weeks, resolve any production issues, and help you plan the next iteration.
Select a function to see how AI agents reduce manual workload and improve outcomes in that domain.
AI agents handle the rules-heavy, repetitive work that ties up finance teams, from invoice matching to close-of-period reporting.
AI agents handle the rules-heavy, repetitive work that ties up finance teams, from invoice matching to close-of-period reporting.
Support agents resolve multi-step queries without escalation, accessing policy documents, account data, and product knowledge in a single interaction.
Operational agents monitor, co-ordinate, and escalate across supply chain data, reducing the manual work of keeping complex logistics moving.
Sales agents qualify leads, enrich CRM records, and draft outreach at a speed no human team can match, without sacrificing the personalisation that converts.
HR agents reduce the administrative workload on talent teams so they can spend more time on the work that requires human judgement.
Clinical agents handle documentation, scheduling, and administrative load so clinicians can focus on care.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate production-grade agent engineering from demo-quality builds.
Every agent we build includes error handling, retry logic, rate limiting, fallback behaviour, and observability instrumentation from the first sprint. We do not build demos that need to be rebuilt for production.
We select the orchestration framework, memory strategy, and LLM that best fits your use case, not the ones we know best. LangGraph for stateful workflows, CrewAI for role-based multi-agent systems, AutoGen for code-heavy tasks: the right tool for the right job.
Agents are only valuable when connected to the systems your business runs on. We have deep experience integrating agents with ERPNext, Salesforce, HubSpot, custom REST APIs, SQL databases, and document management systems.
We do not split strategy from delivery. The team that designs your agent architecture also builds, tests, and deploys it. You get continuity of context from scoping call to go-live, with no hand-off loss.
“Unlike boutique dev shops that build to spec without strategic context, or large consultancies that advise without shipping, Perimattic brings both capabilities to every engagement.”
AI agent development is the process of designing and building software systems that can perceive their environment, reason about a goal, select and use tools, and take actions autonomously to complete complex tasks. Unlike a standard API call to an LLM, an AI agent maintains state across steps, chooses which tools to invoke, handles exceptions, and iterates until the task is done.
A chatbot responds to a single message with a single response, following a predefined script or an LLM completion. An AI agent takes on a goal and works towards it over multiple steps: calling APIs, reading documents, writing to databases, and making decisions along the way. A chatbot tells you the weather. An agent books your flight, checks your calendar, finds accommodation, and sends you a summary.
A multi-agent system is an architecture where a lead orchestrator agent breaks a complex task into subtasks and delegates each to a specialist agent. Each specialist has its own tools, scope, and instructions. The orchestrator collects the results and synthesises a final output. This pattern handles workflows too broad or complex for any single agent, and allows parallel execution of independent subtasks.
A proof-of-concept agent for a well-scoped use case typically takes two to four weeks. A production agent with full integration, error handling, and observability typically takes six to twelve weeks depending on integration complexity. Multi-agent systems for enterprise workflows run from ten to twenty weeks for initial deployment. We provide a more accurate estimate after the free scoping call.
Costs depend on the complexity of the workflow, the number of integrations, and whether you need a single agent or a multi-agent system. A focused single-workflow agent starts from around USD 15,000. A multi-agent system covering several business functions is typically USD 40,000 to USD 120,000. We scope every engagement before quoting so there are no surprises.
We are framework-agnostic. Our team works with LangChain, LangGraph, CrewAI, AutoGen, Haystack, and Agno depending on the requirements. For LLM foundations we use GPT-4o, Claude 3.5, Gemini 1.5, Llama 3, and Mistral. For memory and retrieval we use Pinecone, Weaviate, pgvector, Qdrant, and Redis. We select the stack based on your use case, not our defaults.
Yes. We have deep experience connecting agents to ERPNext, Salesforce, HubSpot, SAP, and custom REST APIs. For ERPNext specifically, we can expose ERP data as agent tools and allow agents to read, write, and trigger workflows within the ERP. We handle authentication, rate limiting, and data validation as part of the integration work.
RAG, or Retrieval Augmented Generation, is the technique of retrieving relevant documents or data from a knowledge base and providing that context to the LLM before it generates a response. For agents, RAG is the primary mechanism for grounding decisions in your specific business data: product catalogues, policy documents, customer records, and historical transactions. Without RAG, agents hallucinate or give generic answers.
Every agent we build includes guardrails that constrain what actions the agent can take, input and output validation, maximum step limits, human-in-the-loop escalation points for high-risk decisions, and comprehensive logging of every decision the agent makes. We establish evaluation baselines before go-live and monitor accuracy, latency, and failure rates in production.
We have delivered AI agent projects for clients in financial services, healthcare, insurance, manufacturing, logistics, real estate, and professional services. Our processes and integration patterns are designed to meet the data sensitivity and compliance requirements common in these sectors. We can discuss sector-specific requirements on the free scoping call.
Tell us about your workflow and we will show you exactly where an AI agent can reduce manual workload, improve throughput, and deliver a measurable return in the first quarter.