AI workflows that take the busywork off your team — and keep a human where it matters.

Yes. A significant share of our AI work is built for other agencies and consultancies under NDA. Three partnership models: white-label (your brand, our engineers, fully invisible), agency-of-record (a dedicated AI pod working as your in-house capacity), and capacity overflow (sprint-by-sprint engagement when your team is full). Code ownership transfers to your repositories, and we run inside your tooling as standard. Agents and automation are exactly the kind of work clients are now asking for and agencies are least often staffed to deliver safely.

Our entire team is based in Noida, India — 40 people in our iThum Tower B office, founded in 2015. We work with businesses and agencies across the UK, US, Ireland, Australia, the UAE, Germany, and the Netherlands. Working hours overlap with UK mornings, the full EU workday, and US afternoons. For agency partners we run in their tooling as standard, and every engagement has a named project lead as a single point of contact.

Usually both. Traditional automation handles the deterministic, structured steps — faster, cheaper, and perfectly reliable. An AI agent handles the steps that need judgment over messy input. The strongest design is a rule-based workflow with an agent dropped in only at the specific points that genuinely need a model. Handing an entire process to one agent is slower, costlier, and harder to trust than it needs to be. We map the workflow and tell you which steps want rules, which want an agent, and which want a person.

AI workflows range from a single focused agent or pipeline through to a process-wide automation touching several systems. The scope drivers are the number of steps automated, the number of tools and systems the agent must integrate with, the messiness of the inputs, and the accuracy and safety bar. We scope every engagement against the specific brief, are competitive with established engineering rates internationally, and are honest about which steps to automate first. A process audit is the lowest-commitment way to get a costed roadmap.

A focused agent or document pipeline is typically an 8 to 14 week engagement, depending on how many tools it integrates with and how high the reliability bar must be. A process audit is 2 to 4 weeks. We work in two-week sprints with weekly demos, and because the eval harness and guardrails are built first, the completion-rate number is visible the whole way through. More complex multi-step workflows take longer, and we phase them so the highest-value steps ship first.

On the right steps, yes — and that is the key qualification. A well-engineered agent runs unsupervised on low-stakes, reversible steps and pauses for human approval before anything irreversible. Reliability comes from the engineering around the loop: step and cost limits, validated tools, guardrails, full tracing, and evaluation on whole-task outcomes. An agent with that is safe to leave running on the work it owns; an agent without it is not. We design every workflow so the autonomous parts are genuinely safe and the risky parts always reach a person.

With guardrails designed in from the start. The agent is given a limited, deliberately chosen set of tools, so it cannot act outside that set. Dangerous and irreversible actions — sending external messages, moving money, deleting data — are gated behind a hard requirement for human approval. Step and cost limits stop a runaway loop. Every action is traced and reviewable. The safety of an agent comes from these engineered controls, not from trusting the model to always behave — and we treat that safety standard as a named, verifiable SLO.

It means the agent runs autonomously on the steps that are safe and reversible, and pauses for a person to approve anything that is not — sending, paying, deleting, committing. It is a deliberate design feature, not a limitation. A good human-in-the-loop workflow gives the approver full context and a clear interface, so the review takes seconds, and it logs every decision. The aim is not an agent that never needs a human; it is an agent that asks for one at exactly the right moments and handles everything else on its own.

Yes — that is the normal case. An AI workflow acts through tools, and those tools are connections to your existing systems: your CRM, your database, your ticketing system, your APIs. We build those connections as validated, typed functions with clear boundaries, so the agent works inside the systems you already run rather than replacing them. Your systems stay your systems; the workflow adds an engineered layer that reads from and acts on them, with guardrails on anything that changes state.

Three kinds of work should stay with a person. High-stakes, irreversible decisions — sending money, legal commitments, final hiring calls. Anything where an error would be expensive and hard to catch, because nothing would be checking it. And work that needs genuine accountability or empathy. AI can assist all of these — drafting, suggesting, surfacing options — but it should not own them. Deterministic, structured steps should also not go to AI: rules do them better. We are honest about this on every engagement, because automating the wrong thing is the most common AI workflow mistake.

On whole-task outcomes, not single steps. We build an evaluation set of real tasks with known-good results and score the workflow against it on every change, with a CI gate that blocks releases below the completion and accuracy floors. In production we track task completion rate, cost per task, and the safety standard, and we capture every failure and feed it back into the eval set. The result is a workflow that gets measurably more reliable every month — rather than one that quietly drifts and nobody notices until it matters.

Yes, and AI workflows need it. They run every day, take actions, and meet new inputs constantly. We offer ongoing engagements covering monitoring for runaway loops and cost spikes, eval-set growth from real production failures, guardrail review as the workflow’s tools and scope change, model A/Bs and swaps, and dashboards for completion rate, cost, accuracy, and safety. A running agent is a system that needs tending — not a build you walk away from.

AI Integration is adding AI features to an existing product — RAG, search, copilots. AI Workflow — this page — is automating a process with AI, including multi-step agents that take actions across your systems. AI Product is building a product where AI is the core, not an add-on. They share the same engineering foundation — tools, evals, guardrails, observability — and many engagements touch more than one. Our AI Engineering hub page is the place that ties all three together.