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Vatsal Shah

AI Integration for HubSpot, Salesforce & Microsoft Dynamics

Add AI features — copilots, summarization, search, scoring — directly inside the CRM and ERP your team already uses, without rebuilding your platform.

Talk to Vatsal

Most Canadian mid-market teams don't need a new system — they need AI inside the system they already paid for. We add copilots, record summarization, AI search, and lead or account scoring directly into HubSpot, Salesforce, Microsoft Dynamics, and Monday.com. The features live where your team already works. Most engagements ship the first AI feature inside your CRM in 3–4 weeks.

Fit for

  • Canadian businesses with an existing CRM or ERP (HubSpot, Salesforce, Microsoft Dynamics, Monday.com, NetSuite) and at least 12 months of historical data in it
  • Teams with a specific job to be done — summarize call notes, score leads, find similar past deals, draft outreach, surface at-risk accounts
  • Operators who want AI inside the tool the team already opens every morning, not a separate app

Not a fit for

  • Companies looking to replace the CRM itself — that's a different and much larger engagement
  • Teams whose CRM data is sparse or unstructured to the point where there's nothing meaningful to ground the AI on
  • Use cases that need a brand-new product surface rather than a feature inside an existing one

What you walk away with

  • An AI feature deployed inside your CRM or ERP — visible to users on the record, deal, ticket, or account view they already use
  • Native installation through the right path for the platform: HubSpot UI extensions and serverless functions, Salesforce Lightning components and Apex callouts, Dynamics Power Platform extensions, Monday.com apps
  • Bidirectional sync with your CRM data — the AI reads the records, writes back summaries or scores, and respects your existing permissions and field-level security
  • Prompt and model configuration documented so your admin can change tone, thresholds, or behavior without us in the room
  • An eval harness for the AI feature with 30+ real historical records, so quality is measured rather than vibed
  • Usage and quality monitoring — how often the feature is used, where it adds value, where it gets overridden by humans

How the engagement runs

  1. 1

    Discovery · Week 1

    We work through the records, the user's daily flow, and the specific moment where the AI should show up. Pull sample data, define what good output looks like, agree on the success metric.

  2. 2

    Architecture · Week 2

    Where the feature lives in the UI, which model handles it, how it reads and writes data, how permissions are respected. You see the integration surface and the cost-per-call estimate before we build.

  3. 3

    Pilot build · Weeks 3–4

    Working AI feature installed in a sandbox or dev environment of your CRM. Tested against real historical records. Weekly demo, weekly course-correct.

  4. 4

    Production deployment · Week 5

    Phased rollout to a single team or user group in production, with usage and quality monitoring already wired. Expand once the metrics hold.

  5. 5

    Optimization · Weeks 6–8

    Tune against real production usage, refine prompts and thresholds, hand off admin documentation so your team owns the feature end to end.

By industry

For Canadian Financial Services

AI inside the CRM and AMS at credit unions, MGAs, and brokerages — member or client record summarization, next-best-action prompts on account view, AI search across policy and product documents, churn-risk and renewal scoring on book-of-business. Respects field-level permissions and existing approval workflows.

For Canadian Healthcare

AI inside the practice management and CRM systems used by multi-site clinics, dental groups, and physiotherapy networks — patient-record summarization for the front desk, referral and recall scoring, AI search across clinical protocols and provider notes. Tuned for your provincial framework.

For Canadian Customer Operations

AI inside HubSpot, Salesforce, Zendesk, Front, and Intercom — ticket and conversation summarization, lead scoring grounded in real historical conversions, account-health snapshots on the account view, AI-drafted follow-ups that pull from real CRM context. The AI surfaces in the same place the rep already works.

Selected work

Recent CRM and ERP integration work.

“Vatsal is an excellent full stack developer and highly skilled project manager. He identified our business needs quickly and established a very strong framework. His incredible speed should be noted, this is a developer who doesn't waste time and hit every target date we threw at him.”
Josiah Liesemer
Josiah Liesemer
IT Specialist and Developer, Zucora Home
Read more in our engineering log →

Frequently asked

Is this a plug-in or a custom build?
Custom built for your data and workflow, installed through the platform's native extension path. HubSpot UI extensions and serverless functions, Salesforce Lightning components with Apex callouts, Dynamics Power Platform solutions, Monday.com apps. It looks and behaves like a native feature, but the prompts, models, and integrations are yours.
What does it cost?
Pricing is custom per engagement and depends on the platform, the number of AI features, integration complexity, and expected usage volume. We share pricing on the first discovery call. Inference costs are passed through transparently — no markup on tokens.
Where does the data live?
Your CRM or ERP remains the system of record. The AI reads from it and writes back to it. For Canadian data residency, we deploy the inference layer in AWS ca-central-1, Azure Canada Central, or Bedrock Canada, and choose model providers that match your residency requirements.
Which platforms do you work in?
HubSpot (Sales, Marketing, Service, Operations Hubs), Salesforce (Sales Cloud, Service Cloud, Experience Cloud), Microsoft Dynamics 365, Monday.com, NetSuite, Zoho, Pipedrive. For tools without a strong extension model, we build the AI feature as a side panel or browser extension that pulls and writes through the API.
Will this break when the platform updates?
We build against the platform's supported extension APIs, not against scraped UI. Major updates from HubSpot, Salesforce, or Microsoft are tested against the eval harness before they roll out. If something breaks, the runbook tells your admin how to identify it and we have a defined response SLA in the engagement.
Do we own the code?
Yes. Everything ships into your repository from commit one — the extension, the prompts, the eval cases, the deployment scripts. If you bring on internal engineers later, they pick up exactly where we left off.
How long does a first feature take?
Three to five weeks from kickoff to a first AI feature in production for a single team. Subsequent features inside the same platform move faster — most of the integration plumbing is reused.
Who does the work?
Two to three engineers from our Toronto-based team, led by Vatsal. The people who scope the engagement are the people who write the code. The full team is named on our team page — you can see and talk to them before we start.