Marxen Cloud · Agents

Agents, at work.

A catalogue of enterprise AI agents Marxen builds and deploys. The business problem each was built to solve, what it does, and what changes when it runs.

33 entries · VI sections · Live and in build

Wooden card-index cabinets in a library catalogue hall, brass cup handles and labelled drawer fronts, standing on a marble floor — Library card index · catalogue hall

Foreword

Built for the work that was never worth a person's day.

Every entry here starts the same way. A capable team spending its hours on work that is necessary but not valuable: compiling, researching, formatting, reconciling, re-keying between systems that were never designed to speak to each other.

These agents do not replace judgement. They remove the preparation standing between people and their judgement. A relationship manager walks into a meeting already briefed. An analyst opens a finished draft instead of a blank page. A support agent answers in the moment rather than after a search.

What makes ours different

Any vendor can sell you an agent. Three things separate a Marxen deployment.

It runs inside your perimeter.
This is not a SaaS tenant with your data sitting in it. Your models, your infrastructure, no foreign API in the data path. For banks, hospitals and government departments this is the difference between a pilot and a signed contract.
It speaks the languages your customers actually use.
Our support and voice agents handle Tamil, Hindi, Telugu, Malayalam, Bengali and code-switched English because Marxen Data Labs collects that speech ourselves. Vendors who buy multilingual capability off a shelf get the standard variety of a language and fail on the one people actually speak.
It is tuned on your material, not the internet.
Retrieval runs over your contracts, your circulars, your case history. When an answer is wrong you can see which document caused it.

How we deliver

Discovery and workflow mapping. Build against a defined success measure agreed before we start. Deployment inside your environment. Then handover, with documentation and runbooks, so your team owns it.

Typical first agent: eight to twelve weeks from kickoff to production.

VI sections

The catalogue

33 entries

Filter by status

Filter by function

IRevenue & Sales Intelligence7 entries

Agents that compress the research and preparation behind every deal, turning fragmented account data into meeting-ready intelligence.

The problem: Account executives and SDRs research accounts by hand across the CRM and data warehouse, compile engagement signals, build account plans, draft outreach and prepare slides. It repeats weekly, for every priority account, for every seller.
What it does: Prioritises accounts on a weekly cadence, generates an intelligence summary and opening plan for each, drafts outreach, and prepares the pitch deck. A multi-step weekly ritual becomes a brief waiting on Monday morning.
What changes: Hours per account per rep return to selling. Account planning becomes consistent across the team instead of varying by whoever owns the account.

The problem: Teams manually sweep tender portals, GeM, departmental sites and private RFQ sources every working day. Opportunities are found once a day at best, and the ones posted after the sweep are found too late.
What it does: Monitors every relevant source continuously, identifies qualifying opportunities against your criteria, and surfaces them in real time with the bid documents attached.
What changes: The daily manual sweep disappears. Discovery moves from once-daily to continuous, which matters most on short-window tenders where being second to see it means not bidding.
Marxen note: Built with Indian public procurement in mind. GeM, state portals and departmental notice boards are handled natively rather than treated as generic web sources.

The problem: Reps assemble attendee context, account background and discovery angles from scattered places before every first meeting. Preparation quality varies enormously across a large sales organisation, and the reps who need it most prepare least.
What it does: Produces a complete first-meeting brief on demand: who is in the room, what the account has been doing, suggested discovery angles and a proposed agenda.
What changes: Preparation time drops and, more usefully, becomes uniform. Every meeting gets the standard of prep your best rep does, not the average.

The problem: Reps research senior prospects across professional networks, press, interviews and podcasts to infer how they communicate and how they decide. It is slow, and the read is inconsistent between reps.
What it does: Profiles an executive from public signal: communication style, decision tempo and a behavioural read, with the sources it drew from.
What changes: Outreach approach becomes deliberate instead of guessed. Consistent across the team rather than dependent on who happened to do the reading.

The problem: Building an account snapshot means pulling financials, strategy and organisational context from fragmented sources, ahead of planning, outreach or an executive review.
What it does: Generates a depth-controlled account snapshot covering financials, strategy and organisational structure, in a consistent format every time.
What changes: Research time falls, and the depth of an account snapshot stops depending on how much time the rep had that week.

The problem: Duplicate prospect records clog the CRM. Company research is manual, summaries are inconsistent, and internal qualification drags because nobody trusts the data.
What it does: Ingests prospects from the CRM, de-duplicates, enriches from the web, scores qualification against your criteria, and writes a structured company summary for every qualified prospect.
What changes: Duplicate processing and manual research both drop sharply, and every prospect entering the pipeline carries the same standard of intelligence.

The problem: Commercial banking RMs research companies, monitor market news and prepare account plans by hand. Cross-sell opportunities are missed because the signals sit across internal and external sources that nobody has time to join.
What it does: Automates company research, monitors live market news against the portfolio, drafts account plans, and surfaces cross-sell triggers. The RM opens an intelligence briefing before every client interaction instead of a blank page.
What changes: Less time per account-review cycle, and cross-sell signals arrive at the point of need instead of surfacing after the meeting.
Marxen note: Deployed on-premise. Customer data, portfolio positions and account plans never leave the bank's network, which is what makes this deployable under RBI expectations rather than stuck in review.

IIMarketing & Brand4 entries

Agents that produce brand-aligned content and creative at a fraction of the manual cycle, without scaling the team.

The problem: Internal comms run through a multi-step manual workflow: design coordination, content review, iterative feedback. Long turnaround, heavy effort per piece, and a queue that grows faster than it clears.
What it does: Handles the workflow from brief to approval-ready creative, including newsletters and leadership communications, with brand rules applied automatically.
What changes: Turnaround compresses from days to hours. Across a few hundred communications a year, weeks of approval and revision cycles disappear.

The problem: Teams write and rewrite web copy, case studies and long-form assets by hand, then spend more effort dragging everything back into brand guidelines. Slow cycles, no scalability.
What it does: Generates brand-compliant web copy, case studies and long-form assets on demand, applying brand guidelines at generation so output arrives near-final rather than needing a rewrite.
What changes: Review cycles shorten substantially, and consistency stops depending on which writer picked up the brief.

The problem: Campaign content is created and managed manually across channels, which slows production and caps multilingual reach. Most teams solve the language problem by not solving it.
What it does: Produces multichannel campaign content with native multilingual output and search optimisation applied at the point of creation, not bolted on afterwards.
What changes: Campaign production accelerates and multilingual reach becomes routine rather than a special project.
Marxen note: Our multilingual output is built on speech and text corpora we collect ourselves. The difference shows in Indian languages, where generic tools produce technically correct copy that reads like a translation.

The problem: Agencies managing reputation for a hundred-plus clients do it through individual platform logins, assistant-drafted responses and fragmented monthly reporting. Capacity cannot grow without hiring.
What it does: Triages reviews, drafts responses, runs approval workflows and produces branded monthly client reports, consolidating several point tools into one system across internal users and client accounts.
What changes: Time-to-respond drops, reporting becomes automatic, and the agency onboards more clients without headcount rising in step.

IIICustomer Experience & Support3 entries

Agents that resolve, route and assist across voice and chat, cutting handling time while protecting service quality.

The problem: Agents retrieve information across fragmented systems while the customer waits. Handling times run long, and the cost base of a large support organisation is enormous relative to the value of the average interaction.
What it does: Real-time call transcription, live retrieval across product knowledge, autonomous handling of first-line tickets, and partial automation of second-line workflows. Deployed as an in-call copilot with a queryable interface for instant lookup.
What changes: Average handle time falls, first-contact resolution rises, and the agent stops apologising for the search.

The problem: Staff read and answer email queries one at a time and field repeat questions on usage and purchasing. Round-the-clock coverage is impossible without night shifts nobody wants.
What it does: An agent pair handling email and chat at all hours, with a human-in-the-loop escalation path for anything it cannot close.
What changes: Hours per person per day return to the work that needs a person, and coverage extends to every hour without adding a shift.

The problem: Service teams handle inquiries manually across voice and chat, with volume spiking hard in season. Staffing for the peak means overstaffing for the year.
What it does: Handles routine inquiries end to end across voice and chat, with human escalation for the rest. Designed to absorb seasonal volume without proportional staffing.
What changes: Faster resolution, lower handle time, and peak capacity that does not need to be hired for.
Marxen note: This is the agent where the Indic advantage is most visible. Voice systems built on foreign corpora fail on Indian accents, code-switching and the way people actually phrase a complaint. Ours are trained on speech our own network collected, in the field, in the conditions the agent will meet.

IVResearch & Competitive Intelligence11 entries

Agents that read, synthesise and surface insight from large and fragmented knowledge estates.

The problem: A firm holds thousands of long-form research reports. Nobody reads twenty pages to answer one question, so the back catalogue sits unused while the firm pays to produce more of it.
What it does: A conversational layer over the entire research estate. Users ask and get a grounded answer with citations back to the source report.
What changes: Time to insight collapses from reading a report to asking a question. The archive becomes an asset instead of storage.

The problem: Competitive intelligence means analysts searching multiple sources by hand and compiling updates on a cycle. Hours per analyst, and the monitoring is only as consistent as the analyst's week.
What it does: Automates competitor research across sources and assembles a consistent intelligence report on a fixed cadence.
What changes: Research hours per cycle drop sharply and the reporting becomes uniform, which is what makes it comparable period to period.

The problem: Subject-matter experts search large file stores of documents and spreadsheets to answer internal and customer queries. As volume grows, turnaround slows and the team caps how many requests it accepts.
What it does: A sensing layer over fragmented internal knowledge, letting experts and executives research and reach informed decisions in near real time.
What changes: Query resolution moves from days to near-immediate, and the expert team's ceiling on request volume lifts.

The problem: A deal-origination team compiles a hundred-plus company profiles a month from eight to ten fragmented sources. Each profile can take a full working day, leaving little bandwidth for the evaluation the team actually exists to do.
What it does: Automates the research, enriches across sources with enforced source prioritisation, and generates a structured one-pager. Fragmented tools consolidate into one system of record.
What changes: Per-profile time falls dramatically, data quality improves because source hierarchy is enforced rather than remembered, and leadership queries resolve in seconds.

The problem: Planners interpret audience data across several sources by hand. Cycles are slow, outputs differ between planners, and cultural insight rarely survives the journey into activation-ready strategy.
What it does: A decision-intelligence agent and dashboard that standardises how audience data becomes activation-ready media strategy, with consistent decision logic across the planning team.
What changes: Planning cycles shorten, subjective variation between planners drops, and insight arrives in a form the activation team can act on.

The problem: Research, advisory and strategy teams need to explore a large estate of case studies conversationally, with directional answers and structured narratives, rather than searching datasets by hand.
What it does: Delivers grounded answers, structured strategic narratives and case-level exploration across the case-study estate.
What changes: Exploration becomes a conversation. Findings that previously required knowing what to search for now surface by asking.

The problem: Building and maintaining an analyst contact database means manual research across websites and professional networks, and the database is stale within a quarter.
What it does: Researches and assembles analyst contact records automatically, keeping the outreach database complete and current, and generating event lists on demand.
What changes: Manual search time falls and the database stops decaying between refreshes.

The problem: Analysts extract insight from source documents and turn it into presentation-ready summaries by hand. Significant time per deliverable, capping how many requests fit inside standard turnaround.
What it does: Extracts insight from source material and assembles presentation-ready summaries, so the analyst starts from a draft rather than a blank document.
What changes: Throughput rises within the same turnaround window, and analyst effort redirects toward analysis rather than formatting.

The problem: Persona development and concept testing depend on recruitment, moderation and scheduling. Weeks pass before a team learns something it needed before the creative was made.
What it does: Generates research-grounded synthetic consumers from segmented research and first-party data, then runs simulated focus group discussions for directional response on creative and messaging.
What changes: Persona creation and early-stage testing move from weeks to hours, with traditional research reserved for the decisions that genuinely need it.
Honest framing: The output is directional. It replaces the third round of concept testing, and it will not stand in for a study you would defend to a board.

The problem: Competitor reporting is manual, inconsistent and dependent on fragmented sources, so the insight is out of date by the time it is circulated.
What it does: Automates competitor intelligence end to end, gathering across review platforms, professional networks and company pages, and producing a consistent report on a set cadence.
What changes: Manual effort drops substantially and reporting arrives on time and in the same shape every period.

The problem: Planners and strategists analyse fragmented data from multiple third-party research sources, spending significant time synthesising it into something a plan can be built on.
What it does: Generates contextual research insight across multiple sources on demand, producing planning-ready summaries from a prompt.
What changes: Analysis time falls and insight generation becomes consistent rather than dependent on which sources the planner happened to check.

VOperations, Finance & Back-Office5 entries

Agents that automate the procedural core of the enterprise: procurement, billing, resourcing and engineering operations.

The problem: HR answers the same employee questions across time zones. Developers struggle to navigate large codebases and hold consistency. QA runs long manual regression cycles with incomplete coverage. The three functions look unrelated and have the same shape of problem underneath.
What it does: Three coordinated agents: an HR assistant resolving employee queries, a development assistant for codebase navigation and consistency, and a QA assistant that compresses regression cycles and extends coverage.
What changes: HR workload drops sharply and query resolution becomes immediate. Development velocity rises. QA cycles shorten by weeks while coverage widens, which shows up later as fewer production bugs.

The problem: Teams use external AI tools with no governance, no shared workflows and no way to scale anything they build. Output is inconsistent, sensitive material goes places nobody approved, and none of it compounds.
What it does: A secure multi-model platform that centralises AI usage and lets teams build, share and scale agents across internal work and client engagements.
What changes: Shadow AI consolidates into one governed environment. Work built by one team becomes available to the next instead of being rebuilt.
Marxen note: Deployed inside your infrastructure. This is usually the first thing a CISO asks about and the reason most workspace tools do not get past review.

The problem: Billing analysts validate data across multiple systems and documents before an invoice can be raised. Long cycle times, heavy manual effort, and an audit trail assembled after the fact.
What it does: Validates data across systems and prepares invoices automatically, reducing manual touches per case to near zero and recording the validation as it goes.
What changes: Cycle time falls, manual touches per case drop from several to almost none, and audit readiness becomes a property of the process rather than a project.

The problem: Supplier onboarding, sourcing, contract management and operations each require manual coordination across systems and teams. Slow cycles and no visibility across the supplier-to-payment lifecycle.
What it does: An end-to-end procurement system spanning supplier discovery, compliance monitoring, negotiation assistance and workflow automation across the full lifecycle.
What changes: Supplier onboarding and sourcing cycles shorten materially, supplier discovery widens beyond the incumbents, and the lifecycle becomes visible in one place.

The problem: Resource planning is manual with no real-time view of skills, availability and workload. The result is predictable. Some people are overloaded, some are underused, and projects slip for staffing reasons rather than technical ones.
What it does: Optimises allocation with live visibility into skills, availability and workload, supporting faster and more accurate staffing decisions.
What changes: Manual planning effort falls sharply and utilisation evens out across the bench.

VIPeople, Strategy & Innovation3 entries

Agents that accelerate decisions about talent, ideas and direction.

The problem: Exit interviews are collected inconsistently and documented manually. Data quality is poor, participation is low, and by the time a trend is visible the people who could have told you about it have gone.
What it does: Standardises and automates exit data collection, then analyses trends in real time.
What changes: Participation rises, collection becomes uniform, and attrition patterns surface while they can still be acted on.

The problem: Executive hiring spreads across email, documents and conversations with many stakeholders. Recruiters reconstruct candidate status and decision context by hand, and senior candidates are lost to delay.
What it does: Centralises recruitment intelligence into one queryable view covering candidate status, interview progress and decision context.
What changes: Manual status tracking disappears, stakeholder back-and-forth shrinks, and decisions get made with the full picture in view.

The problem: Innovation teams research and assess submitted ideas across market opportunity, scalability, ecosystem fit and competitive landscape. It is slow, and it limits how many ideas get a fair hearing.
What it does: Automates the research and applies standardised scoring to internal submissions, with the reasoning visible behind each score.
What changes: Evaluation cycles speed up, scoring becomes comparable across submissions, and the innovation pipeline becomes visible to the people funding it.

Your problem is the next entry.

Every agent here started as a conversation about work that was costing a team its time. If something above resembles your own, that is where we begin.

We would rather have a conversation about your version of this than run a platform demo.

Talk to us info@marxen.in

Built in India. For the people actually using it.

Tell us what you are trying to do. Bring the use case, the constraints and the users. We will tell you honestly whether Marxen is the right call, including when the answer is no.

Book a callinfo@marxen.in

Agents, at work.

Built for the work that was never worth a person's day.

What makes ours different

It runs inside your perimeter.

It speaks the languages your customers actually use.

It is tuned on your material, not the internet.

How we deliver

The catalogue

IRevenue & Sales Intelligence→7 entries

01Account Intelligence SystemSales & marketingIn build

02Tender & RFQ Monitoring AgentSales operationsIn build

03First-Meeting Brief AgentSales intelligenceIn build

04Executive Profile AgentSales intelligenceIn build

05Account Research AgentSales intelligenceIn build

06Prospect Data Intelligence AgentSales operationsIn build

07Relationship Manager IntelligenceBankingIn build

IIMarketing & Brand→4 entries

08Internal Communications AgentMarketing automationIn build

09Content Marketing AgentMarketingIn build

10Campaign Content AgentMarketing automationIn build

11Reputation & Engagement PlatformReputation managementIn build

IIICustomer Experience & Support→3 entries

12Support Copilot & Autonomous AgentsCustomer supportIn build

13Support Email & Chat AgentsCustomer supportIn build

14Voice & Chat Service AgentCustomer supportIn build

IVResearch & Competitive Intelligence→11 entries

15Research Intelligence AssistantResearchIn build

16Competitor Watch AgentMarket intelligenceIn build

17Decision Intelligence AgentConsultingIn build

18Deal Origination & One-Pager AgentDocument automationIn build

19Media Planning Intelligence DashboardMedia planningIn build

20Case-Study Discovery AgentResearch intelligenceIn build

21Analyst Database AgentResearchIn build

22Research Synthesis AgentResearchIn build

23Synthetic Focus Group AgentResearchIn build

24Competitive Intelligence Reporting AgentCompetitive intelligenceIn build

25Media Planning Research AgentMedia planningIn build

VOperations, Finance & Back-Office→5 entries

26Multi-Agent System for HR, Engineering & QAInternal operationsIn build

27Enterprise AI WorkspaceEnterprise enablementIn build

28Invoice & Billing Automation AgentFinanceIn build

29Procurement Operating SystemProcurementIn build

30Resource Allocation & OptimisationWorkforce managementIn build

VIPeople, Strategy & Innovation→3 entries

31Exit Interview Intelligence AgentHuman resourcesIn build

32Executive Recruitment Intelligence AgentTalent intelligenceIn build

33Innovation Evaluation AgentStrategyIn build