Research · Economic

The economics of an assistant, role by role.

Tokens are a cost, not a value. We value an assistant by how much faster a defined role reaches a defined outcome, and we price the product so that value is shared with the customer and the company stays profitable from the first subscription.

Method

Time-to-outcome: a two-week study any company can run

The framework behind ELP GPT Business pilots. It needs no data science team and no access to message content.

  1. Define the role and the outcome. An outcome is a unit of finished work: a first-draft proposal, a resolved support ticket, a graded assignment with feedback, a reviewed pull request.
  2. Baseline for one week. Staff log start and finish times for each outcome using their current tools. Ten to thirty outcomes per person is enough.
  3. Assist for one week. The same staff, the same outcome types, with ElpGPT Business and the relevant plugin enabled.
  4. Compare medians, not means. Report the change in median time-to-outcome, the share of outcomes that needed rework, and staff-reported confidence. Means are dominated by the hardest cases.
  5. Translate to money. Value per month = outcomes per month × minutes saved × loaded cost per minute. Compare against the plan price. The pilot should pay for itself inside the second week or it is not a fit.

The independent literature points the same way. Field studies published in 2023 found productivity gains of roughly 14% for customer-support agents with an assistant (Brynjolfsson, Li and Raymond), around 40% faster professional writing tasks (Noy and Zhang, Science), and a 12% increase in task completion with 25% faster completion for consultants on tasks inside the model's competence (Dell'Acqua et al., Harvard Business School working paper). The same consultant study found performance fell on tasks outside that frontier, which is why our plugins narrow the assistant to the work it is good at.

Findings

Where the value concentrates

Patterns from our own pilots and from published studies. Figures are ranges, not guarantees; every organisation should run the two-week study.

Ranges combine ELP pilot observations with the published field studies cited above. Gains shrink or reverse on tasks outside the assistant's competence; measure before you scale.
RoleOutcomeTypical change in time-to-outcomeWhat drives it
Research and analysisSourced brief on a market or competitor35–55% fasterLive search, source handling, structured summaries
Customer supportResolved ticket, first reply15–30% faster; larger for newer staffDrafting, policy recall, tone
Sales developmentQualified first meeting booked20–40% more outreach per hourAccount research, personalised drafts, follow-up discipline
EducationLesson plan with differentiated materials30–50% fasterExplanations at several levels, question banks, rubrics
Software developmentReviewed change merged10–30% faster on review and debuggingCodebase explanation, diagnosis, test drafting
OperationsProcedure documented and followed25–45% fasterRunbooks, checklists, connected-tool actions with approval

Unit economics

Why ELP GPT is profitable from the first customer

The CMO's version is simple: we do not buy a brand, we build a model that returns money. Here is the operating version.

Price above cost to serve

Every plan is priced above its blended cost per answer, including search, agents and connectors. Free is capped per day so it is a sample, not a subsidy.

Routing is the margin engine

Cost policies route each turn to the cheapest provider that meets the quality bar for that task. As providers compete, our cost falls and our price holds.

Acquisition paid in product

Referral credits cost us messages we already serve at marginal cost, not cash. Share cards and link previews acquire customers for the price of rendering an image.

No growth-at-a-loss

We do not run paid acquisition at negative contribution. Growth comes from use, referrals and business accounts that passed their own two-week study.

Capital for capacity, not burn

Proposed infrastructure investment in Ontario is for owned capacity that lowers cost to serve. It is financed against demand, not spent ahead of it.

Investor returns from operations

Returns to shareholders come from operating cash flow. That is a different company from one that returns capital only on an exit.

Statements about pricing and routing describe how the product is operated. Financial figures published elsewhere on this site are company-supplied, forward-looking where marked, and not independently audited. Nothing on this page is investment advice or an offer of securities.

Run the two-week study with us

We will help you define outcomes, set up the pilot and read the results.