Custom AI agent development end to end: assistants for customer support, sales, document processing and analytics. RAG systems built on your own data, multi-agent orchestration, integrations with CRM, ERP and accounting systems over API and the MCP protocol. Not a magic neural network — a specific tool that takes routine off your team and runs 24/7 with measurable quality.
An AI agent is not "ChatGPT with a chat window". It is a system built on a large language model, with access to your data through retrieval, tools it can call to take action, memory across conversations, and integrations into the systems you already run. Which of the three you need depends entirely on how many steps and how many systems the process touches.
One agent, one job: answering customers on your site, triaging inbound email, qualifying leads, or an assistant inside a Telegram bot. Includes a RAG layer built on your own documentation and integration with one or two external systems. The fastest way to find out whether AI actually helps your business.
A team of specialised agents that hand work to each other: one qualifies the lead, another drafts the proposal, a third books the meeting. Deep integration with your CRM, ERP and internal knowledge base over API and the MCP protocol, with quality control on every handover.
Full AI infrastructure: dozens of agents across departments, private on-premise LLM deployment for sensitive data, custom fine-tuning on your corpus, full analytics, security audit, SOC 2-aligned controls and a dedicated support team.
A production agent is not a bare model API. It is an engineered system with eight parts: the model, the system prompt, a retrieval layer, callable tools, MCP integrations, memory, guardrails and monitoring. Miss any one of them and you get a demo, not a colleague.
The engine is a large language model, and picking it is an engineering decision, not a preference. Frontier models for hard reasoning, compact ones for speed and cost, open-weight models you host yourself when data cannot leave your perimeter. We benchmark on your actual tasks before committing.
Who the agent is, how it speaks, what it is allowed to do and what it must refuse. Tone of voice matched to your brand — expert, friendly or formal. This is what separates a strange chatbot from something your customers accept as part of your team.
Retrieval-Augmented Generation: your documentation, FAQs, contracts and policies are indexed into a vector store. Before answering, the agent retrieves the relevant passages and answers from them. This is the single most effective cure for hallucination.
Function calling turns a talker into a worker: creating CRM records, issuing invoices, booking meetings, sending email, querying inventory, generating documents. Each tool is a defined function with an explicit contract the model can reason about.
Model Context Protocol is the emerging standard for connecting agents to external systems. Instead of a pile of bespoke wrappers you get standardised connectors to CRM, ERP, mail and databases — and the agent reads live data without retraining anything.
The agent remembers previous conversations with a given customer, their preferences and order history. Short-term memory within a conversation, long-term memory across them. That is the difference between a service desk and starting from zero every time.
Protection against prompt injection, topic boundaries so the agent stays inside its competence, content filters, strict isolation so one customer never sees another's data, and per-user spend limits. Without guardrails an AI agent is a liability, not an asset.
A dashboard covering conversation volume, resolution rate, most frequent questions, failures and user ratings. Every interaction logged for audit and for improving prompts and retrieval over time — plus token spend tracking so scale does not surprise you.
Agents work best where there is a high volume of repetitive tasks with clear rules but enough variation that rigid templates fail. Here are twelve areas where the business result is measurable — with the typical effect two to four months after rollout.
A 24/7 agent that answers common questions, searches your documentation and escalates only the cases that genuinely need a person. Connected to your order database, it knows the customer's history, checks delivery status and starts returns.
The agent qualifies inbound leads through conversation — need, budget, timeline, contact details — filters spam and books meetings for the hot ones straight into your calendar. Your sales team stops doing triage.
A copywriter tuned to your brand voice: social posts from a brief, email sequences, product descriptions for e-commerce, SEO articles to a given structure. Not generic filler — your tone, your terminology, your examples.
Screening CVs against your criteria, running a first conversational screen and ranking candidates. Recruiters spend their time on the shortlist instead of sorting hundreds of applications by hand.
Contract review that flags deviations from your standard position, precedent search across a case database, and first drafts from templates. Lawyers move to strategy instead of first-pass reading.
Company-wide search across policies, procedures, meeting notes, email and contracts. Ask in plain language, get a specific answer with a link to the source. People find things in seconds instead of asking three colleagues.
An analyst connected to your warehouse: ask for "sales by region this quarter versus last year", the agent writes the SQL, runs it and returns a chart with the outliers called out. No SQL knowledge needed on the asking side.
A shopping assistant that understands vague requests — "a red work bag, leather if possible, under $150" — shows matching products, answers spec questions and compares options. Conversion rises because people find the thing instead of browsing.
First-contact agent for patients: collects symptoms and history, answers routine questions, routes to the right specialist and books the appointment. It never diagnoses — it walks the patient to a clinician with the context already gathered.
Processing invoices, statements and acts; spotting anomalies in transaction flows; preliminary scoring from unstructured data; and a customer-facing assistant that explains products without a queue.
A tutor that adapts to the learner's level, explains hard concepts simply, marks homework with reasoning and generates individual exercises. Works as a 24/7 supplement to live teaching, not a replacement for it.
Code review against your own conventions, test and documentation generation, alerts on risky changes, and help writing complex database queries. Connected to your repository through MCP.
Three packages with a fixed starting price. STARTER is one agent for one job and the fastest way to validate the idea. PROFESSIONAL is a multi-agent system with orchestration, MCP integrations and evaluation. ENTERPRISE is private infrastructure with self-hosted models, fine-tuning and a security audit.
An AI project is not shaped like ordinary development: most of the effort goes into iteratively improving answer quality, tuning prompts and retrieval, and testing against real cases. The first working MVP arrives in two weeks; quality is built up in measured steps after that.
We map the process you want automated, count the volume and estimate what share is realistically automatable. If the numbers do not work, we say so at this stage — before anyone signs anything.
Collecting your documentation, cleaning it, chunking and indexing into a vector store. In parallel we agree API access to the systems the agent will act in. This step usually determines the whole timeline.
A functioning MVP you can talk to: prompt, retrieval, first tools, guardrails. From here everything is measured against your own historical cases rather than against a demo script.
The part nobody advertises and everybody needs. We run the agent over hundreds of real past conversations, find where it fails, fix retrieval and prompts, and repeat until the resolution rate stops moving.
Gradual rollout with a human fallback always visible, dashboards live from day one, and two months of close observation while we keep improving the knowledge base.
A current AI engineering stack: leading language models, production agent frameworks, vector databases for retrieval, Model Context Protocol for standardised integrations, function calling, fine-tuning and evaluation frameworks — chosen per project rather than by habit.
| Models | Frontier commercial LLMs for reasoning-heavy work · compact models for latency and cost · open-weight models for self-hosted deployments |
| Agent layer | Function calling · tool orchestration · multi-agent handover · Model Context Protocol (MCP) connectors |
| Retrieval | Vector databases · hybrid search · re-ranking · chunking strategies tuned per document type |
| Backend | PHP · Python · Node.js · REST and webhook integrations · queue-based processing for bulk workloads |
| Integrations | CRM and ERP systems · Google Workspace · Telegram, Slack and web widgets · payment and shipping providers |
| Quality | Evaluation harness on historical data · regression suites for prompts · logging and audit trail · token spend monitoring |
| Security | Prompt-injection defence · PII handling policies · per-tenant data isolation · on-premise deployment where required |
Three projects with concrete business outcomes. AI is not magic — it is a tool with measurable ROI. On request we can walk you through the architecture diagrams and implementation details, with client domains masked.
The firm received primary documents from clients in every format imaginable — scans, phone photos, PDFs, and occasionally a photograph of a screen. Accountants re-typed them by hand. The agent recognises the document, extracts counterparty, amounts, VAT and dates, validates them against the register and posts the entry, flagging anything it is not confident about for human review.
Tender documentation runs to hundreds of pages, and most of it is irrelevant to any given bidder. The agent monitors published tenders, reads the documentation, checks the requirements against the company's capabilities and drafts a proposal from the parts that matter. Estimators review and adjust instead of reading everything from scratch.
Four specialised agents share one memory: mail triage and drafting, calendar negotiation, research briefs on request, and a memory agent that keeps the context of every ongoing topic. The result is not "AI answering email" but a working chief of staff — which freed enough capacity to add a fifth business vertical without expanding the support team.
Describe the process that eats the most time. We come back with an automation option, a price and a timeline — and we say so if the numbers do not work.