AI Agent Development &Intelligent Automation

Most AI agent projects take 6-12 weeks from discovery to deployment, depending on complexity and integration requirements. We provide detailed timelines during the discovery phase.

Yes, our AI agents are designed to integrate seamlessly with existing CRM, ERP, database, and workflow systems through APIs and custom connectors.

We work with your existing data including historical conversations, process documentation, transaction records, and business rules. We also help identify and collect additional data if needed.

We implement rigorous testing, validation frameworks, human oversight mechanisms, and continuous monitoring to maintain high accuracy rates above 95%.

We provide 3 months of optimization support, performance monitoring, regular updates, and team training. Extended support packages are available for ongoing maintenance.

Four things, roughly in order of impact: how many systems the agent has to integrate with, how much of your process is documented versus tribal knowledge, whether the work needs a human approval step, and how accurate it has to be before you will trust it in production. A single-purpose agent reading one system is a very different scope to one that writes back into your CRM and ERP. That is why engagements start with discovery rather than a quote.

Build in-house if AI agents are becoming core to your product and you can keep two or three engineers on it permanently — the maintenance never stops, because models and APIs change underneath you. Work with an agency if you want a first system in production in weeks rather than quarters, or if you want to prove the business case before committing headcount. Plenty of clients do the first project with us and take it in-house afterwards, which is why we hand over the code and documentation.

A chatbot answers. An agent acts. A chatbot can tell a customer your refund policy; an agent can look up the order, check it against the policy, issue the refund in your payment system and write the outcome back to the CRM. The distinction matters commercially, because agents need permissions, audit trails and guardrails that a chatbot never does.

We build on GPT-4 and Claude most often, and we design the model layer so it can be swapped. Model quality and pricing move quickly enough that hard-wiring one provider is a liability. Where a task is narrow and high-volume, a smaller or open model is frequently cheaper and faster than a frontier one, and we will say so rather than defaulting to the biggest option.

Layered constraints rather than a single safeguard: the agent only gets the permissions the task needs, irreversible actions sit behind an approval step or a confirmation, every action is logged so you can audit what happened and why, and outputs are validated against your business rules before anything is written back. We also agree up front what the agent must never do, and enforce that in code rather than in the prompt.

You do. The source, the prompts, the configuration and any fine-tuning data are yours, handed over at the end of the engagement along with documentation for your own team. Your data is not used to train anything shared with other clients.

Conventional software tests cover the deterministic parts — integrations, permissions, error handling. For the model-driven parts we build an evaluation set from your real cases, including the awkward ones, and score the agent against it so you can see the accuracy rather than take our word for it. Most agents then run in shadow mode against live traffic, taking no action, until the numbers hold up.

Running cost is mostly model usage, and it scales with volume rather than sitting fixed. It is worth modelling before you build: a task that runs a thousand times a day on a frontier model has a very different monthly cost to the same task on a smaller one. We estimate this during discovery so the business case includes it, and we design prompts and caching to keep it down.

That is usually the sensible route. Pick one process with a measurable cost, agree what success looks like in numbers before any code is written, and build only that. A narrow first agent reaches production faster, and gives you a real basis to decide whether the next one is worth funding. It also surfaces the integration and data problems early, while they are still cheap.