We Build AI Agents & RevOps
for SaaS Companies.
Pipeline forecast is the sales rep's best guess. Customer health score doesn't exist. Support tickets answered by whoever's free. Salesforce rebuilt its entire positioning around Agentforce. Every SaaS company is building agents internally before selling them externally. The question is which builds you need first.
AI Agents SaaS Companies Are Deploying in 2026
Salesforce rebuilt its entire positioning around Agentforce and is hiring deployment strategists in 9 US cities. Every modern SaaS company is building agents internally before selling them externally. Apollo, Clay, 11x, and Artisan all scaled around the outbound agent in 2025. Every one of these can be built for your company.
Resolves tier-1 support tickets end-to-end using your documentation and known solutions, asks clarifying questions when needed, escalates to a human only when genuine judgment is required, and writes post-resolution notes automatically. Support team spends its time on complex and high-value cases, not password resets.
Enriches target accounts from multiple data sources, researches each prospect's specific context, writes personalised outreach calibrated to their situation, sequences the follow-up, and books meetings directly into your AE calendar. Your SDR team focuses on conversations, not research and first drafts.
Updates CRM records after every interaction, flags deals that have gone stale or have missing next steps, identifies forecast risks, writes weekly pipeline commentary for leadership, and keeps data quality at a standard that makes forecasting actually useful rather than politically charged.
Monitors product usage signals in real time, identifies accounts whose behaviour pattern matches historical churn indicators, drafts personalised intervention playbooks for the CSM, schedules QBR outreach for at-risk accounts, and escalates red accounts before they become churned accounts.
Answers natural-language questions about product performance — "why did activation drop last week?" — by running queries across your analytics stack, building the chart, and writing a structured explanation with hypotheses and recommended next steps. Product and growth teams get answers in minutes, not analysis requests queued for a data analyst.
Listens to sales calls, identifies coaching moments, scores calls against your methodology, generates personalised feedback for the rep, surfaces objection patterns across the team, and flags deals with risk signals the AE may have missed. Sales managers coach from insights, not by listening to every recording themselves.
Implementations & Automations
Structured RevOps and automation builds — defined scope, timeline, and starting price.
Revenue Operations
The CRM, pipeline, and outbound infrastructure that makes your revenue motion data-driven rather than anecdotal.
Sales, marketing, and CS operating on separate systems with no shared view of the customer. Handoffs break deals. Forecasts are opinions.
Single source of truth across the revenue team. Handoffs become structured. Forecast becomes a number you can actually defend to the board.
SDRs spending 3 hours a day on research and personalisation that produces inconsistent output. Sequence management manual. Activity data unreliable.
Personalised sequences built and executed automatically. SDR time shifts from research to conversations. Activity data becomes trustworthy.
Marketing passing every form fill to sales as an MQL. Sales ignoring most of them because quality is low. Trust between the two teams is gone.
Leads scored against firmographic and behavioural signals. Sales only sees genuinely qualified opportunities. MQL-to-SQL conversion improves measurably.
Free trial users hitting activation milestones with no outreach. PQLs identified late or not at all. Sales doesn't know who to call until the trial has already expired.
Product signals trigger the right outreach at the right moment. PQLs surfaced to sales in real time. Trial-to-paid conversion improves without additional headcount.
Customer Success & Retention
For SaaS companies where NRR matters as much as new ARR — the onboarding, health monitoring, and churn prevention layer.
Onboarding driven by whoever has time. Milestones tracked informally. Time-to-value inconsistent across customers and CSMs.
Structured onboarding sequence runs automatically. Milestones tracked. CS only intervenes when a customer is at risk of not reaching value. Time-to-value drops measurably.
CS team finds out an account is churning when the cancellation email arrives. No early warning. No systematic way to prioritise which accounts need attention this week.
At-risk accounts flagged weeks before churn, not after. CS prioritises proactively. NRR improves because the right interventions happen at the right time.
NPS surveys sent quarterly. Results sit in a spreadsheet. Nobody synthesises the open-text feedback. Product and CS make decisions without knowing what customers actually said.
Open-text feedback clustered automatically by theme. Sentiment trends visible in real time. Product roadmap decisions informed by what customers said, not what someone remembered.
Support & Self-Service
For SaaS businesses where support cost as a percentage of revenue is a metric that matters to the board.
Support team handling 200 tickets a day where 140 are variations of 15 recurring questions. Every ticket consumes the same human time regardless of complexity.
60–80% of tier-1 volume resolved without human involvement. Support team capacity freed for complex, high-value cases. CSAT maintained or improved.
Sales team finding out about competitor moves from prospects on calls. Product team learning about competing features 6 months after launch. No systematic way to stay current.
Competitor pricing, feature, and messaging changes flagged automatically. Battlecards updated in real time. Sales and product always operating on current intelligence.
Revenue Intelligence & Forecasting
For revenue leaders who need forecasting to be a data exercise rather than a negotiation exercise.
Weekly forecast call is 45 minutes of deal-by-deal negotiation between sales managers and reps. The number submitted is a political compromise, not a data-driven prediction.
Forecast generated from actual CRM signal — stage progression velocity, engagement data, deal age. Board-ready revenue intelligence available in real time, not weekly.
Sales manager has 8 reps and 40 recorded calls per week. Coaching is whoever they randomly listen to. Objection patterns across the team invisible. Rep improvement inconsistent.
Every call scored automatically. Personalised coaching feedback generated per rep. Objection patterns surfaced across the team. Manager coaches from insights, not surveillance.
Not sure which scope to start with?
Most SaaS companies start with a Starter build, validate the ROI in 3–4 weeks, then scope the full RevOps layer.
Starter
One focused build. NPS intelligence, lead scoring, or competitive monitoring — results before committing to the full RevOps layer.
Growth
Full RevOps CRM with connected sales, CS, and product data — plus at least one AI agent. The revenue team starts operating from real data.
Enterprise
Custom agents, Agentforce implementation, deep product-data integrations, revenue intelligence, and ongoing RevOps optimisation.
They built exactly what we needed without overcomplicating it. The system has been running for months without us having to touch it.
Every SaaS build is led by one of us directly.
SaaS RevOps implementations involve product data integration, complex pipeline logic, and CS workflows that most CRM consultants configure incorrectly. Shivam Kapoor and Sonam Malhotra are Salesforce and HubSpot certified and active on every project — you get the founders, not a consultant who learned CRM last year.
Know exactly what your SaaS
company needs to build. In 30 minutes.
Free audit. We map which agents and RevOps implementations fit your stage and stack, in what order, and what outcome to expect.
What SaaS companies ask us — answered straight.
· Amroar Technologies · All Industries