Practical notes from the engineers who build agents, copilots and AI products.
UK and EU clients get GDPR-aligned processing terms, SCCs for transfers, UK-hour overlap and USD or GBP invoicing by arrangement. The full London working day falls inside the Bengaluru afternoon and evening, so stand-ups and demos run in normal hours, and data can stay in a UK or EU region when residency matters.
Saying no in week two saves a client six months; we tell you when the answer is buy, wait or do not build. A meaningful share of the ideas that reach our discovery sprint leave with a recommendation other than "build it with us", and we think that recommendation is the most valuable thing a consultancy can sell.
A fixed-price discovery answers feasibility and cost for a known fee that is credited to the build, so the first step never costs twice. Each later program has a fixed scope, price and date, which moves the risk of estimation onto the vendor and gives you a clean exit point after every stage.
Implementations fail on configuration that copies the demo, dirty data, slide-deck training and nobody measuring adoption after go-live. The software usually works; people route around it. The fix is configuring to real tasks, cleansing data before migration, training on tasks and tracking usage at 30, 60 and 90 days.
Bengaluru combines senior AI engineering talent, cost efficiency and time-zone overlap with the UK and US East, making it a natural AI build centre. Its depth comes from two decades of product engineering, a working-day overlap with London and the US morning, and a cost base that keeps fixed-price programs fixed.
AI systems degrade as providers update models, data shifts and prompts age; evaluation regression and retraining loops keep them working. AI model drift in production is a condition you manage, not a bug you fix once: a golden eval set run on every change, production sampling, pinned model versions and a named owner.
Pilots die without owners, evals, integration and running budgets; scope for production from day one and pilot on real data. Decide before the pilot what production would require, build the pilot inside those constraints, and set a dated decision point with the evidence needed to proceed written down in advance.
Insist on ownership of code, infrastructure, prompts and fine-tuned weights on payment, with pre-existing tools licensed to you. The contract should also name the evaluation sets, training data derivatives and documentation as deliverables, because those are what let you run and change the system without the vendor.
WhatsApp agents handle order status, returns within policy and reorders with live Shopify and courier data. The difference between a shopping bot customers abandon and one they use is whether the agent can read the order, act within a written policy, take a payment, and hand off to a person with the context intact.