Low call coverage
Managers review a small fraction of recorded calls, usually the ones that someone flagged. Most coaching signal never reaches the rep, and the calls that decide the quarter are rarely the ones reviewed.
All solutions
Sales Intelligence
Custom AI Deal Coaching & Sales Intelligence Agents
Engineered for B2B sales teams, banks, wealth managers, insurers and professional-services firms running complex, multi-stakeholder pipelines. Replace rigid off-the-shelf coaching tools with bespoke AI agents built exactly around your sales methodology (e.g., MEDDICC, BANT) and CRM stack.
The operational reality of deal coaching
Most sales organizations know coaching works. The problem is coverage, consistency and visibility, and those do not scale with manager time.
Inconsistent scorecards
Two managers score the same call differently. Without a rubric applied the same way every time, coaching feedback cannot be compared across reps, teams or quarters.
Lost deal visibility
The real state of a deal sits in call transcripts, email threads and CRM notes. Leadership sees stage and amount, not intent, urgency or blockers, so risk surfaces late.
Generic tools lack your methodology
Conversation-intelligence platforms such as Gong and Clari are strong at transcription, search and forecasting roll-ups. They are not built to enforce your own qualification logic, so the methodology still lives in a slide deck.
Why off-the-shelf sales software falls short
Packaged tools are built for the average sales team. If your process, data rules or deal structure are distinctive, you end up bending the process to fit the product.
Seat-license costs that scale with headcount
Per-seat pricing rises with every rep and manager you add, while most teams use a fraction of the feature set.
Rigid out-of-the-box scorecards
Packaged scorecards assume a generic sales cycle. Long, multi-stakeholder enterprise cycles and regulated sales conversations rarely fit them without heavy workarounds.
Third-party recording silos
Calls and transcripts live in a vendor's environment. For firms with strict data governance, that is another system of record to review, approve and monitor.
Use cases and industries
The common thread is regulated, relationship-led sales: long cycles, many stakeholders and rules about what can be said. That is also why these teams outgrow packaged tools. Each build is scoped to your methodology and policies.
B2B enterprise & SaaS sales
Score calls against MEDDICC, BANT or your own qualification framework, generate instant deal briefs, and track objection handling across reps and quarters. Technology and SaaS teams fit here too.
Financial advisory & wealth management
Streamline client and prospect review calls, support advisor oversight, and scale qualitative review from a small sample of interactions to every call.
Commercial banking & lending
Give relationship managers and loan officers fast answers from an objection knowledge bank and next-best-action guidance, aligned with your internal risk and compliance policies.
Insurance: brokers, agencies & benefits sellers
Support long, multi-stakeholder sales cycles, give producers renewal and cross-sell prompts, and check recorded calls against consent and suitability rules.
Law firms & professional services
Support partner-led business development where no sales manager exists, debrief pitches and RFPs, and bank the client questions nobody could answer.
Related work: voice AI intake for law firmsCommercial real estate & brokerage
Track deal-by-deal pipelines with long cycles and many parties, and keep the history of every conversation attached to the deal.
How it works: system architecture and execution flow
A short, observable pipeline. Each stage has one job and leaves a record you can review.
Step 1: Automated call ingestion & multi-agent transcript processing
Calls and transcripts are pulled from your call-intelligence tool and CRM. Specialised agents extract structured facts from each conversation, validated against a fixed schema before anything downstream reads them.
Step 2: Methodology compliance check
Each call is scored against a fixed rubric built from your methodology. Scoring rules are explicit and versioned, so the same call produces the same score.
Step 3: Risk & objection flagging
Intent, urgency, blockers and unanswered objections are extracted and flagged. Questions nobody could answer are routed to the team and banked for reuse.
Step 4: Rep prioritization & actionable deal brief
Deals are ranked by rules you control, and each rep gets a short brief: what happened, what is at risk and what to do next. Human owners keep the final call.
Core feature modules
Each engagement is scoped to your workflow. These are the building blocks we most often assemble.
Automated Deal Scoring
Score every deal and call against your rubric, with deterministic rules and hard overrides deciding high-stakes outcomes rather than a model alone.
Objection Knowledge Bank
Unanswered prospect questions are routed to the team, resolved once and banked, so the next rep who hits the same objection already has an answer.
Real-Time Deal Briefs
A concise brief per deal, covering intent, blockers, risks and next steps, generated from the call and CRM record instead of assembled by hand.
Multi-Call Historical Tracking & Trend Analysis
Track scores, objections and deal movement across calls and reps over time, to see which coaching changes actually move outcomes.
Build vs. buy: when to build custom AI coaching
Custom is not always the right answer, and we will tell you so. Packaged software is often enough when most of the first list is true. A custom build earns its cost when several of the second apply.
Off-the-shelf is enough when
- Your sales process is close to a standard framework and a packaged scorecard fits with light configuration.
- Your team is small enough that per-seat pricing is cheaper than a build.
- You mainly need transcription, search and call libraries, not custom scoring logic.
- Third-party hosting of call recordings already clears your data governance review.
Build a custom agentic system when
- You run a proprietary sales framework, or a hybrid of MEDDICC, BANT and your own criteria, that packaged scorecards cannot express.
- Data governance or regulation limits where call data can live and who can process it.
- Deals are complex and multi-stakeholder, and the signal sits across calls, CRM notes and documents.
- You need deterministic, reproducible scoring and an audit trail for how each score was reached.
- Seat licenses, or the workarounds around your current tool, now cost more than owning the system.
Engineering transparency
Engineering transparency and security
Technical buyers should know what they are buying. The systems are built in Python, with schema-validated model outputs, modular tool-calling and lean cloud infrastructure whose cost scales with call volume, not seats.
Python services, modular tool-calling
Agents are small, single-purpose Python services that call tools through explicit interfaces, so each step can be tested, replaced and audited on its own.
Deterministic where it matters
Language models extract and summarise. Scoring, ranking, routing and forecasting use plain rules, so the numbers leadership reports reproduce from one run to the next.
Data isolation by design
Call data is handled inside your own cloud account with scoped access. We minimise what leaves it, and what is sent to a model provider is limited to what a step needs.
Security posture
- Commercial model APIs do not train on your data by default. We confirm each provider's current terms in scoping, and use zero-retention options where a provider offers them.
- Consent and opt-out rules can be enforced as hard vetoes, ahead of any model output.
- Logging of inputs, scores and decisions supports review and audit.
- Designed for enterprise compliance readiness: we build to your security and compliance requirements.
Engagement model, timeline and cost drivers
Every build follows the same four-step engineering discipline: Map, Bound, Instrument, Govern.
01
Map
We map how your team sells today: the methodology, how calls are recorded and reviewed, what the CRM holds and where deal signal gets lost.
02
Bound
We define what the agents will and will not do: the rubric they score against, the rules that override a model, and the decisions that stay with people.
03
Instrument
We build with logging, monitoring and cost visibility from day one, so every score, flag and brief can be traced and measured.
04
Govern
We hand over with controls, documentation and an operating runbook, so your team can change the rubric and stays in charge of the system.
Timeline depends on scope, and we give a fixed scope and estimate after a technical scoping call. The factors that move a project are:
- Number of CRM, call-recording and data systems to integrate, and what each one exposes through an API
- Complexity of your methodology and the number of rubrics or scorecards to encode
- Call volume and transcript sources
- Consent, suitability and data-governance rules the system must enforce
- Dashboards, reporting and approval workflows
Verified proof from prior work
Two engagements delivered by members of our team, with the details published as case studies.
Prior work by our team
Two-Agent AI Sales Intelligence Suite
Deal scoring and call coaching for a B2B sales organization, with hard rules deciding every high-stakes call.
- Pipeline scored
- $50M
- Modules shipped
- 7
Prior work by our team
AI Sales Coaching Agent for Wealth Management
Automated scoring of every sales call and a compounding objection knowledge bank for a US SEC-registered investment advisor.
- Call coaching coverage
- 5% to 100%
- CEO time returned
- ~240 hrs/yr
Delivered by members of our team before Canvas Labs Partners was founded. About this work
Frequently asked questions
No. We build on commercial model APIs, which do not train on customer data by default, and we confirm each provider's current terms with you during scoping. Where a provider offers zero-retention options, we can use them. The system runs in your own cloud account, so call data is not copied into a separate vendor silo.
Integrations are built per system, using direct APIs where they exist. Prior work connected a CRM API and a call-intelligence API. Scope depends on which tools you use and what each one exposes, and we confirm that on the scoping call.
We start from your methodology, whether that is MEDDICC, BANT or your own framework, and turn it into an explicit rubric with weighted criteria. We then test it against calls your managers have already scored and tune it until the scores match their judgement. Your team keeps the rubric and can change it.
Not on its own. Models extract and summarise, while ranking and high-stakes outcomes are governed by rules you control, with hard overrides. Your managers keep the final call.
Running costs are mainly model API usage and modest cloud hosting, and they scale with call volume rather than seat count. Many teams take the handover and run it in house. Others keep us on a light retainer for monitoring, rubric changes and API updates. You own the code, configuration and documentation, and the system is designed so a competent Python team can maintain it without us.
Both depend on scope. The main drivers are the number of CRM and call systems to integrate, how complex your methodology and rubrics are, call volume, and the consent and data-governance rules to enforce. We give a fixed scope and estimate after a technical scoping call.
Ready to scope AI deal coaching around your methodology?
Talk directly to the engineers who would build it. Bring your sales framework, your CRM and call stack, and your data-governance requirements, and leave with a scoped plan.