A leading product engineering company, creating adaptive software solutions to improve operations, providing businesses with expert development services from across domain.
A leading product engineering company, creating adaptive software solutions to improve operations, providing businesses with expert development services from across domain.
Sales / CRM
A complete Before-Sales and After-Sales CRM platform — AI-powered market insights, Kanban pipeline management, complaint tracking, and role-based dashboards built and delivered by Creuto end to end.
2
CRM Modules (Before & After)AI
Market Intelligence Engine360°
Customer Lifecycle Coverage0
Leads Lost to SpreadsheetsClient
Enterprise Sales Organisation
Industry
Sales Technology / CRM
Services
CRM Platform Development
AI Integration
UI/UX Design
Backend Architecture
Workflow Automation
Platform
Sales CRM AppSales teams at growing businesses face a persistent structural problem: the tools they use to manage leads, track conversations, and close deals are either generic, disconnected, or built for a different kind of organisation entirely. The result is lost revenue, poor visibility, and a team that spends more time maintaining spreadsheets than selling.
A mid-to-large enterprise engaged Creuto with a precise brief: build a unified CRM platform that manages the full customer lifecycle from first lead capture through to post-sale support — and uses AI to surface actionable market insights that improve targeting and campaign decisions.
What Creuto built:
Before-Sales CRM with lead capture, pipeline management, Kanban and List views, and a multi-view dashboard
After-Sales CRM with customer tracking, complaint management, ticketing, and warranty workflows
AI features including location-based demand insights, smart ad targeting, predictive lead scoring, and campaign recommendations
Centralised customer database, role-based dashboards, reporting, analytics, and workflow automation
This was a full product engagement. Creuto handled product scoping, UX design, backend engineering, AI integration, and cloud deployment as a single, end-to-end team under a fixed-scope contract.
Lead management was running on spreadsheets
The sales team was tracking hundreds of leads across disconnected Excel files and email threads. There was no single system of record, which meant leads fell through the cracks, follow-up timing was inconsistent, and new sales hires had no reliable onboarding context.
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No centralised lead database — data lived across individual reps' files
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New leads entered manually with no standard intake workflow
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Duplicate entries and missing fields created unreliable data
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Lead ownership was ambiguous, causing internal conflicts
Managers had no real-time view of where deals stood. Pipeline reviews required individual reps to prepare manual status updates, which were always out of date by the time leadership reviewed them.
Poor follow-up discipline and no after-sales support
Without automated reminders or structured activity tracking, follow-up depended entirely on individual rep behaviour. High-value leads were contacted once and abandoned. Once a customer made a purchase, they entered a support black hole.
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No automated follow-up scheduling or reminder system
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Call and email logs maintained manually, rarely updated
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Complaints tracked through email with no ownership or SLA
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Warranty periods not centrally tracked — disputes required manual research
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Repeat complaints from the same customer went unrecognised
No market intelligence or data-driven strategy
The marketing and sales strategy was based on intuition, not data. The team had no insight into which geographic regions were showing higher demand, which products had the most search interest, or how to prioritise ad spend across territories.
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Ad campaigns targeted broadly with no location-level demand data
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No mechanism to identify high-opportunity regions automatically
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Sales and marketing operated in silos with no shared data layer
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Decision-making was reactive, not predictive
01
Every inbound lead, regardless of source, is captured into a centralised database with a structured intake form. Each lead record holds contact details, source, assigned rep, stage, priority score, and a full activity timeline.
Duplicate detection, search, filter, and sort across the full database by any field. Leads captured from web forms, manual entry, and third-party integrations — all with automatic timestamp and source logging.
02
Two views serve different working styles: a Kanban board with drag-and-drop stage movement (New, Contacted, Qualified, Closed) and a List view with sortable columns and bulk actions.
Stage transitions are logged automatically with timestamps and rep attribution. Pipeline health metrics displayed in summary cards. Filters by rep, region, lead source, date range, and deal value give managers a real-time, reliable forecast with no manual data preparation.
03
Every interaction with a lead — calls, emails, meetings, and notes — is logged with timestamps, outcomes, and next-step scheduling. Automated reminders trigger when follow-up deadlines approach.
Overdue follow-ups surface in a priority queue on the rep dashboard. Manager view shows team-wide activity volume and follow-up compliance. Inactivity alerts flag leads with no activity in a configurable time window.
04
The AI module analyses demand signals by geography and category to help teams prioritise effort and budget. Rather than relying on intuition, teams get data-driven signals about which regions are showing the highest intent for specific products or services.
Location-based demand heatmaps identify high-opportunity regions automatically. Smart ad targeting suggestions surface recommended audiences and formats. Predictive lead scoring ranks leads by estimated conversion probability.
05
Post-purchase customer tracking, complaint management, ticketing system, warranty tracking, and structured issue resolution workflows. Every complaint is assigned an owner with an SLA. Ticket status is visible to both customers and internal teams.
Repeat complaint patterns from the same customer are surfaced automatically — replacing the previous black hole that closed customers fell into after purchase.
06
Every role — sales rep, manager, admin — gets a dashboard tailored to their decisions. Conversion rates, pipeline health, follow-up compliance, activity volume, and regional demand trends are all surfaced without any manual data preparation.
We went from managing leads on spreadsheets to having a full pipeline view, AI-driven territory insights, and a support ticketing system all in one platform. Creuto understood sales operations, not just software development. The AI demand heatmaps alone changed how we allocate ad budget.
Head of Sales, Enterprise Sales Organisation
Generic CRM tools are built for average teams. A purpose-built platform built around your specific sales process, market context, and customer journey performs fundamentally differently.
Most CRMs stop at the deal close. Creuto builds Before-Sales and After-Sales modules as one integrated platform — lead capture, pipeline management, post-sale support, and complaint resolution in a single system.
The AI module surfaces market demand by geography, predictive lead scores, and campaign targeting suggestions. It is built into the platform's data architecture — not a third-party widget bolted on after the fact.
A CRM that reps don't use produces bad data. Creuto designs every rep-facing feature for minimal friction, so adoption is high and data quality follows.
After discovery, Creuto provides a detailed scope document and a fixed-cost proposal. No vague estimates, no change order surprises — just predictable delivery.
Every technology chosen for transactional integrity across customer records, real-time pipeline updates, and AI-grade analytics on demand signals.
From mapping the complete sales lifecycle to deploying AI market intelligence — a structured build that aligned the platform to real sales team behaviour.
Structured workshops with sales reps, managers, and leadership to map the complete lead lifecycle, pipeline stages, follow-up rules, and post-sale support workflows. Defined the AI module's data requirements and market intelligence use cases.
Role-specific dashboards designed for sales rep, manager, admin, and support. Kanban board and list view designed for the pipeline module. AI insights panel designed to surface actionable signals without overwhelming the interface.
Lead management, pipeline state machine, activity logging, and follow-up automation. After-Sales CRM module with complaint ticketing and warranty tracking. Role-based access control and analytics dashboard built in parallel.
Market demand analysis, predictive lead scoring, and ad targeting recommendation engine integrated and validated. Models trained on regional demand data and refined against historical lead conversion data before going live.
End-to-end testing across all CRM workflows, user roles, and edge cases. Sales team involved in UAT to validate pipeline UX and follow-up automation. AI output validated against known market performance data.
Production deployment with full data migration from existing spreadsheets. Sales team training with guided lead and pipeline onboarding. Two-week hypercare window with dedicated support during first live sales cycle.
We sit with sales reps during discovery — not just managers. The biggest signal of CRM success is whether reps actually log every call, every email, every follow-up. Building software they want to use means understanding their workflow, not just the manager's reporting needs.
This CRM moved a sales team off spreadsheets, into a unified pipeline, and onto AI-driven territory targeting — in one engagement.
Everything you need to know about building a product like this with Creuto.
Standard CRM analytics report on your existing pipeline — win rates, deal velocity, rep performance. The AI market insights module analyses external demand signals by geography and category to tell you where high-probability opportunities exist before you have leads there. It is a prospecting intelligence tool, not a reporting tool.
Yes. The platform supports lead capture from web forms, manual entry, and third-party integrations via REST webhooks. Each lead source is tagged automatically so conversion rate by source is always visible without manual attribution.
The two modules share a centralised customer database. Once a deal closes, the customer record transitions seamlessly from the pipeline into the post-sale module with full history intact. Support agents can see the complete relationship history — every interaction from first lead to current complaint — in one view.
A full CRM with Before-Sales pipeline, After-Sales support, and AI market intelligence typically takes 4–6 months from discovery to launch. Timeline varies based on the number of integrations, workflow automation complexity, and the volume of historical data to migrate.
A full-lifecycle CRM with pipeline management, after-sales support, and AI market intelligence typically costs between $40,000 and $100,000 USD depending on AI scope, integration depth, and feature surface. Creuto provides a fixed-cost proposal after discovery.
Whether you have a 5-person sales team or a 500-person organisation — Creuto builds CRM platforms that actually fit how your team sells, support your customers post-sale, and surface where to find your next deal.
Full lifecycle. AI insights. High rep adoption. One team. Fixed price.
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