Dextego's sellers already had call recorders. Dextego wanted to give them a coach. We worked as an extension of their product team, building the platform end to end: the agents, the live-call pipeline and every surface a rep touches.
Call recorders tell a sales rep what went wrong after the deal is gone. Coaching happens in one-to-ones that don't scale, and new reps take months to ramp.
A platform where AI coaches profile the buyer before the call, guide the rep during it, and score the conversation after. Reps can also rehearse against voice roleplay coaches between calls.
As an extension of Dextego's in-house team, on both strategy and delivery: product development, new features and testing, with their founders setting direction.
Scoped with the founders and delivered as releases, with testing built into each one.
A meeting bot joins the call, streams the conversation to the API over WebSockets, and pushes prompts back to the rep while the call is still happening.
Reps pitch to Tego, a voice coach on OpenAI's realtime API, and get a pitch score and a next step when they finish.
Pre-call buyer packs and stakeholder profiles built from public data, with DISC-style communication guidance for each buyer.
Summaries, a scorecard, follow-up advice and a shareable recap, emailed or pushed to the CRM after every call.
Each team's playbooks are parsed from PDF, Word and Excel and embedded, so coaching uses the customer's own methodology.
Points, streaks and badges, plus admin reporting for revenue leaders on who is practising and improving.
The problems that decide whether a product like this ships at all, and what solving each one means for the people using it.
Advice is only useful while the rep can still act on it. After the call, it's just a report.
A meeting bot joins the call and streams the conversation to our backend as it happens. The coaching agents read it in real time and push prompts to whatever the rep has open: web app, extension or desktop.
Guidance in seconds, not in tomorrow's one-to-one
One do-everything AI prompt coaches badly and can't be graded.
Coaching is split into specialist agents (roleplay, Q&A, scorecards and more), each with a narrow brief. Scoring is its own agent, so it can be tested and tuned on its own.
Coaching quality that can be measured and improved
When an AI coach says something wrong to a customer's rep, you need to know exactly why.
Every agent run is traced end to end, and background work such as summaries, emails and CRM updates runs as retryable jobs, so a failed step is retried instead of silently lost.
Debug any answer; nothing dropped between systems
The simple setup that works for ten teams' playbooks becomes the bottleneck at a thousand.
Each team's sales playbooks are indexed so the coaches use the customer's own methodology. We're moving that search onto a dedicated engine in stages, with no big-bang cutover.
Coaching in each customer's own playbook, at scale
Chosen per layer for the job, with managed services wherever they save the client engineering time.
Ioanna describes the team working as an extension of Dextego's own product team — collaborating with their in-house group on both product development and the delivery of new features, across tactical and strategic engineering, testing included.
An AI social media platform for agencies and multi-brand organisations: a month of on-brand posts planned, designed, approved and published across six networks. Built app and AI end to end by one managed pod.
Point at the case study closest to your problem, or just describe it. You'll talk to an engineer from the bench that built these, not a salesperson.