Most AI consulting is billed like any other consulting: by the hour, with token and compute costs passed through on top. That model quietly charges you for experimentation. AI-native Salesforce delivery done properly works the other way round. We burn the tokens; you buy the outcome. Here’s what that means in practice, what it covers, and what it deliberately doesn’t.
The problem with usage-based AI delivery
When a partner bills AI work by the hour and passes through model usage, every experiment lands on your invoice. Prompt iterations, approaches that didn’t work, model comparisons, the long tail of tuning — you fund all of it, whether or not it ever reaches production. That’s a strong incentive for the vendor to keep exploring and a weak one to ship.
It also makes budgeting close to impossible. Usage is spiky and hard to forecast, so the number at the bottom of the invoice moves for reasons you can’t see or control. Finance teams notice, and AI projects that can’t be budgeted tend to stall in proof-of-concept — impressive in a demo, never quite approved for production.
Our model: outcomes, not effort
We flip the incentive. H2TECH absorbs the token and compute spend our own delivery agents use. You’re priced on the scope and outcome we agreed, not on how many tokens it took us to get there. If a mapping needs a hundred iterations to get right, that’s our cost to carry.
That alignment changes behaviour on our side. Because experimentation is on our books, we’re motivated to reach a working, production-ready result quickly and cleanly rather than to bill the exploration. And because our agents do the repetitive work — first-draft field mappings, test data, documentation, regression scenarios — certified engineers spend their time on the decisions that need judgement: architecture, edge cases, and the trade-offs only a person accountable for the result should make.
What we absorb — and what stays yours
Honesty matters here, so let’s be precise. We absorb the AI cost of how we build: the agents, models, and compute we use to analyse, draft, test, and document your solution. What stays with you is what you run: your Salesforce licences, and any Agentforce or other AI consumption your own org uses once agents are live. We help you size and govern that consumption, but we don’t hide it inside a services price.
That split keeps both sides honest. You never pay for our experimentation, and you always know what your production AI will cost to operate.
Speed with governance, not speed instead of it
Agent-accelerated delivery is fast, but fast only helps if it’s also correct. Every output an agent produces on our projects — code, configuration, mappings, agent definitions — is reviewed by a certified Salesforce engineer before it goes anywhere near your org. AI writes the first draft; a human who’s accountable for security, testing, and governance signs it off. We explain why that step is non-negotiable in why every AI output needs an engineer’s sign-off, and how it extends to Agentforce itself in running Agent Script like code.
What it looks like in results
The point of the model is outcomes you can measure. On an Agentforce sales automation project, a 35% increase in pipeline conversion within 90 days and four hours per rep per week handed back to selling. On a CPQ rebuild, quote time down 97%, from four hours to under eight minutes. On a legacy CRM migration, delivery 60% faster than the original estimate with zero data loss. None of those clients paid for the tokens it took to get there.
How it fits our engagement models
The same principle runs through all three ways we work. On fixed-scope projects, AI efficiency shows up as a tighter timeline and a predictable price. With staff augmentation, our certified people bring their agent tooling with them at no extra cost. On managed service, it means more improvement shipped per month for the same retainer. In every case the AI makes us faster; it never makes your invoice less predictable.
What to ask any AI partner
Whoever you work with, ask three questions. Who pays for model usage during delivery? Who reviews AI-generated work before it reaches production, and are they certified? And what will the finished solution cost you to run each month? A partner who answers all three plainly is one you can budget for. If you’re comparing partners more broadly, our Salesforce implementation partner checklist covers the rest.
Key takeaways
- With AI-native Salesforce delivery done our way, we absorb the token and compute cost of building — you pay for the agreed outcome.
- Your licences and production AI consumption stay visible and yours; we help size and govern them rather than hiding them.
- Every AI output is reviewed by a certified Salesforce engineer, so speed never comes at the expense of governance.