An AI agent is software that uses a large language model to decide and act, not merely to reply. Where a chatbot returns text, an agent reads a ticket, looks up the customer in your ERP, checks stock, drafts the response, and books the follow-up — calling real tools in your real systems at each step. We build agents on top of frontier models from Anthropic, OpenAI and Google, connected to your data through the Model Context Protocol and your own APIs, with an approval boundary around anything irreversible.
In short
- Agents act, chatbots reply. An agent calls your APIs, reads your database, updates records and completes multi-step tasks end to end.
- Connected to your actual systems — ERPNext, Laravel applications, CRMs, helpdesks, WhatsApp, Google Workspace, or any documented API.
- Built on MCP. We expose your systems as Model Context Protocol servers, so one integration serves every agent you build afterwards.
- Guardrails are part of the build: permission scopes, human approval on irreversible actions, full audit logging, and hard spending limits.
- Model-neutral. Claude, GPT or Gemini — chosen on cost and measured accuracy for your task, and swappable later without a rewrite.
It Completes the Task
Not a reply and a handoff. The agent reads the source system, decides the next step, calls the tool, verifies the result, and reports what it did.
Wired Into Your Stack
ERPNext, Laravel, MySQL, helpdesk, WhatsApp Business API, Google Workspace. If it has an API or a database, an agent can be given controlled access to it.
Bounded by Design
Scoped credentials, a read-only default, human approval before anything irreversible, and a complete audit trail of every action taken.
Measured, Not Assumed
We define the success criteria before building, then evaluate the agent against a fixed test set so accuracy is a number you can see, not a claim.
Agents We Build
These are patterns we have implemented, not a catalogue of possibilities. Every one of them is scoped to a specific process with a measurable outcome.
| Agent type | What it actually does |
|---|---|
| Support triage agent | Reads an incoming ticket or WhatsApp message, classifies it, pulls the customer record and order history, drafts a grounded reply, and either sends it or queues it for a human depending on confidence. |
| Internal operations agent | Answers staff questions against your ERP and documentation, then performs the follow-up action — creating the purchase request, updating the record, or generating the report. |
| Sales qualification agent | Enriches an inbound lead, scores it against your qualification criteria, writes the CRM notes, and books the call in the right calendar. |
| Document processing agent | Extracts structured data from invoices, contracts, delivery notes or ID documents, validates it against your rules, and posts it into the accounting or ERP system. |
| Reporting agent | Queries your databases on a schedule, assembles the numbers, writes the commentary explaining what moved and why, and delivers it to email or WhatsApp. |
| Developer & DevOps agent | Reads logs and monitoring output, correlates an incident across services, proposes a diagnosis with evidence, and opens the ticket with the context already filled in. |
| Research agent | Gathers information across your internal sources and the open web, cites where each fact came from, and produces a structured brief instead of a wall of text. |
| Scheduled autonomous agent | Runs unattended on a schedule — reconciling data, chasing overdue items, monitoring thresholds — and escalates to a human only when something needs judgement. |
We build agents on top of existing frontier models. We do not train foundation models from scratch, and we will not tell you that we do. What we do is engineer the surrounding system — tools, retrieval, guardrails, evaluation and integration — which is where agent projects actually succeed or fail.
Where an Agent Is the Right Answer
High-Volume Repetitive Work
A task performed dozens of times a day that requires reading context and making a judgement — too variable for a script, too repetitive for a person.
Work That Spans Systems
A process where the answer lives in three different places and someone spends their day copying between them.
Out-of-Hours Coverage
Enquiries and alerts that arrive at 2am and currently wait until morning, where a bounded first response has real commercial value.
Unstructured Input
Free-text messages, scanned documents, PDFs and emails that have to become structured records before anything else can happen.
What You Receive
- A deployed agent running on your infrastructure or ours, integrated with the systems agreed in scoping.
- MCP servers for your systems — reusable integrations that any future agent or internal tool can connect to, owned by you.
- An evaluation set and results — the test cases we measured against, the accuracy achieved, and the failure modes we found.
- Guardrail configuration — permission scopes, the approval boundary, rate limits and the monthly token spending cap.
- Observability — logging of every prompt, tool call and action, so you can audit exactly what the agent did and why.
- Documentation and handover — architecture, runbook, and how to extend it, plus training for whoever will operate it.
How We Build It
- Process mapping — we watch how the task is done today, count the volume, and identify the decision points. If the process is not worth automating, this is where we say so.
- Success criteria — we agree what "working" means as a number, and assemble a test set of real cases before any code is written.
- Tooling and integration — we build the MCP servers and API connections the agent needs, with scoped credentials and a read-only default.
- Agent construction — prompts, tool definitions, retrieval, error handling, and the approval boundary around irreversible actions.
- Evaluation and hardening — we run the agent against the test set, measure accuracy, fix the failure modes, and re-run until it clears the agreed threshold.
- Pilot, then rollout — a supervised pilot with a human reviewing output, narrowing that review as the numbers justify it, then full deployment with monitoring.
Frequently Asked Questions
What is the difference between an AI agent and a chatbot?
A chatbot produces text. An agent produces actions. Ask a chatbot about your order and it explains how to check; ask an agent and it queries the order system, reads your record, and tells you where the shipment is — then updates the delivery address if you ask it to. The difference is tool access and the authority to use it.
Will the agent make mistakes?
Yes, at some rate — and any supplier who tells you otherwise is selling. That is precisely why we measure accuracy against a fixed test set before deployment, keep a human approval step on irreversible actions, and log everything. The correct question is not whether it errs but whether it errs less often than the current process, and whether errors are caught before they cause harm.
Can an agent delete or change our data?
Only what you explicitly permit. We default to read-only access and grant write permissions per tool, per system. Anything irreversible — payments, deletions, outbound customer communication — sits behind a human approval step unless you decide otherwise in writing after the pilot.
Which AI model do you use?
Whichever measures best for your task at acceptable cost. We work with Claude, GPT and Gemini, and we build the integration layer so the model can be changed later without rebuilding the agent. Model pricing and capability move quickly; locking your architecture to one provider is an avoidable risk.
Does our data get used to train the AI models?
Not under the enterprise API terms we deploy on. Data sent through the commercial APIs of the major providers is not used for training by default. Where the data is sensitive enough that this is still not acceptable, we can deploy open-weight models on your own infrastructure — with an honest discussion of the capability trade-off.
What does it cost to run?
Two components: our build fee, and the ongoing model usage measured in tokens. We estimate the running cost from your real volumes during scoping and set a hard monthly cap so it cannot surprise you. For most back-office agents the model cost is a small fraction of the labour cost it displaces — but we will tell you when it is not.
Do we need to replace our existing systems first?
No. Agents connect to what you already run. We have built against ERPNext, Laravel applications, MySQL databases, helpdesks and the WhatsApp Business API. If a system has an API or a database we can read, it can be integrated — a legacy system with no API is the one case that needs a separate conversation.
Who owns the agent when the project ends?
You do. Source code, prompts, MCP servers, evaluation sets and documentation are all handed over. You are not renting access to something we keep.
Find Out Whether an Agent Fits Your Process
Free first call — we will tell you plainly if a simple script would be cheaper
