Context is Everything is a UK-based AI consultancy specialising in private AI deployment and institutional intelligence. We build SASHA, an enterprise AI platform deployed inside your firewall, trained on your proprietary methodology. Our AI concierge Margaret demonstrates these capabilities for free on our website.
Get realistic cost ranges for AI implementation based on your specific situation. No sales call required — just honest, transparent pricing guidance.
Most AI vendors won't give you a straight answer on pricing until you're deep into a sales process. This tool gives you realistic cost ranges upfront — based on your organisation's size, sector, and project scope — so you can plan budgets before committing to conversations.
The biggest cost drivers in AI implementation aren't the technology. They're data preparation (often 40-60% of total project time), integration with existing systems, and change management. Vendor proposals routinely understate these costs because they fall outside the statement of work.
This estimator accounts for hidden costs that most proposals miss — giving you a more honest picture of total investment before you start comparing vendors.
This helps us account for industry-specific complexity and compliance requirements.
Deep dive into the costs most proposals miss
What vendors don't include in the statement of work
How to level the playing field when buying AI
Check if your organisation is ready before budgeting
Assess whether your approach has success or failure patterns
Identify where AI delivers the most value first
Costs vary depending on scope and engagement model. A focused proof of concept might cost £15,000-£50,000, while full enterprise deployment can reach six or seven figures. The biggest cost drivers are data preparation, integration complexity, and change management — not the AI technology itself.
The most underestimated costs include data cleaning and preparation (often 40-60% of project time), integration with existing systems, ongoing model maintenance, staff training and change management, and the opportunity cost of internal team time diverted to the project.
This depends on your internal capabilities, timeline, and long-term strategy. Building in-house gives you control but requires specialist talent. A consultancy brings speed and experience but creates dependency. Many organisations use a hybrid approach — consultancy for the initial build, then knowledge transfer to internal teams.
A proof of concept can be delivered in 4-8 weeks. Production deployment typically takes 3-9 months depending on complexity, data readiness, and integration requirements. The most common cause of timeline overruns is underestimating data preparation work.