Let your sales team sell. Automate everything else.
Pipeline analysis, quote generation, order back-office, forecast reporting — theywork365 sales agents handle the administrative burden so your team spends time on relationships and closing.
-
70%
Less time on admin tasks
-
< 5 min
Quote generation time
-
100%
Pipeline visibility, always
Sales teams lose 30–40% of their productive time to non-selling activities: updating CRM records, building quotes, chasing internal approvals, generating reports. theywork365 sales agents take over this administrative layer entirely, connecting to your CRM, ERP, and M365 tools to work the back-office while your reps work the front.
What Sales agents can do for you.
Each agent is built specifically for your processes and data. Pricing is per action — you pay only for the value delivered.
-
Analyzes your CRM pipeline daily to surface at-risk deals, identify stalled opportunities, flag forecast gaps, and recommend next best actions for each account — delivered to reps and managers every morning.
Before
Sales managers spend 2–3 hours every Monday in CRM trying to understand pipeline health. At-risk deals are identified too late to recover.
After
Every rep and manager starts the day with a clear, prioritized view of their pipeline. At-risk deals are flagged days earlier, improving recovery rate.
How the agent works — step by step
- 1
Agent connects to CRM and reads all open opportunities
- 2
Scores each deal on: days since last activity, close date proximity, stage velocity
- 3
Flags at-risk deals and stalled opportunities with recommended actions
- 4
Calculates forecast accuracy vs. target for each rep and region
- 5
Generates a daily pipeline briefing posted to Sales channel in Teams
- 6
Sends individual deal alerts to account owners for urgent items
- 1
-
Builds accurate, formatted sales quotes from product catalog and customer data in minutes. Handles pricing rules, discount tiers, and customization — sending the draft to the rep for review before delivery.
Before
Creating a quote takes 30–90 minutes per rep, involves checking pricing in multiple systems, and errors happen. Customers wait 1–3 days.
After
Quotes are generated in under 5 minutes. Reps review and send. Customers receive professional proposals the same day.
How the agent works — step by step
- 1
Rep requests a quote via Teams chat or email with customer and product details
- 2
Agent reads customer record from CRM: account tier, history, active contracts
- 3
Pulls product catalog with current pricing, availability, and discount rules
- 4
Assembles quote with correct pricing, terms, and customizations
- 5
Formats output as branded PDF and sends to rep for review
- 6
Logs quote in CRM and links to the opportunity
- 1
-
Handles all post-order administration: order confirmations, shipping notifications, invoice generation, and customer communications — freeing reps from inbox management after a deal closes.
Before
After closing a deal, reps spend hours on paperwork: confirming orders, chasing logistics, sending updates to customers. This kills momentum for the next deal.
After
The agent handles all post-close communication automatically. Reps focus on the next opportunity the moment the contract is signed.
How the agent works — step by step
- 1
Deal marked as closed-won in CRM triggers the agent
- 2
Agent generates order confirmation and sends to customer
- 3
Connects to ERP/logistics system to track fulfillment status
- 4
Sends shipping notification with tracking details when order ships
- 5
Generates and sends invoice at the appropriate milestone
- 6
Alerts rep only if customer responds with a question or issue
- 1
-
Produces weekly sales forecasts by combining CRM pipeline data, historical close rates, seasonal trends, and rep-level performance — delivering a structured report to sales leadership every Monday morning.
Before
Sales forecasts are assembled manually from CRM exports and spreadsheets. The process takes half a day and the result is already outdated by the time it's shared.
After
Leadership receives an accurate, data-driven forecast every Monday at 8am — automatically. The team spends forecast meetings discussing strategy, not updating numbers.
How the agent works — step by step
- 1
Agent runs every Sunday evening from the CRM and historical data
- 2
Calculates weighted pipeline by rep, region, and product line
- 3
Applies historical close rate adjustments by stage and seasonality
- 4
Compares forecast to monthly and quarterly targets
- 5
Generates structured report: committed, best case, pipeline gap
- 6
Posts to Sales Leadership channel in Teams with key callouts
- 1
-
Continuously monitors CRM data quality: identifies duplicate contacts, incomplete records, stale opportunities, and wrong account assignments — and proposes or executes corrections automatically.
Before
CRM data degrades over time. Duplicates multiply, old deals stay open, contacts go stale. Reports become unreliable. Reps lose trust in the system.
After
CRM data quality scores improve measurably within the first month. Reports are reliable. Reps actually trust the system and use it consistently.
How the agent works — step by step
- 1
Agent runs a weekly full scan of the CRM database
- 2
Flags duplicate contacts and accounts with merge recommendations
- 3
Identifies opportunities with no activity in 30/60/90 days
- 4
Detects missing mandatory fields: phone, email, company size, industry
- 5
Auto-corrects formatting issues (phone numbers, email casing, country codes)
- 6
Generates weekly data quality score report for CRM admin
- 1
-
Identifies new prospect companies that match the profile of your best existing customers, using internal data and external signals. Automatically builds targeted prospect lists for outbound campaigns.
Before
Outbound prospecting is based on manually curated lists built from intuition and limited research. The team spends hours building lists with low conversion rates.
After
The agent generates ready-to-use prospect lists based on statistically similar profiles to your top clients. Quality-to-conversion ratio improves and outbound efficiency increases.
How the agent works — step by step
- 1
Agent analyzes existing customer data: industry, size, revenue, growth stage, tech stack
- 2
Identifies top 20% of customers by revenue and retention rate as the ideal profile
- 3
Scans external data sources for companies matching the ideal customer profile
- 4
Scores prospects on similarity, intent signals, and addressable market fit
- 5
Generates a ranked prospect list with company profile, contact suggestions, and fit score
- 6
Delivers list to sales team via CRM enrichment or Teams channel weekly
- 1
-
Analyzes your full contact database and suggests who to reach out to first, based on engagement history, behavioral signals, fit score, and propensity to buy — maximizing conversion rates.
Before
Sales reps decide who to contact based on gut feel, alphabetical order, or whoever emailed last. High-potential contacts go cold while low-priority ones get all the attention.
After
Every rep starts their day knowing exactly which contacts to prioritize. Outreach is focused on the highest-probability opportunities. Conversion rates and rep efficiency improve.
How the agent works — step by step
- 1
Agent reads contact database from CRM: all contacts across accounts and territories
- 2
Scores each contact on engagement recency, email opens, meeting history, and profile fit
- 3
Applies behavioral signals: website visits, content downloads, event attendance
- 4
Calculates a daily priority score for each contact in each rep's territory
- 5
Pushes ranked call and email lists to each rep's dashboard every morning
- 6
Updates scores in real time as new engagement signals arrive
- 1
-
An AI assistant that supports sales reps throughout every deal: suggests next best actions, drafts personalized emails, recommends relevant case studies, and highlights objection-handling responses at each stage.
Before
Reps spend time searching for the right content, drafting emails from scratch, and trying to remember what worked in similar deals. Coaching happens irregularly, if at all.
After
Every rep has an AI co-pilot available at every stage. Emails are drafted instantly. The right content surfaces at the right moment. Deals move faster with fewer coaching hours needed.
How the agent works — step by step
- 1
Rep asks for support via Teams or CRM interface (or agent proactively triggers on deal event)
- 2
Agent reads the deal context: stage, account profile, history, open activities
- 3
Suggests the most relevant next action based on deal stage and account behavior
- 4
Drafts a personalized email or message for the rep to review and send
- 5
Surfaces relevant case studies, ROI data, and competitive positioning for the deal
- 6
Logs suggested actions in CRM and tracks whether they were executed
- 1
-
Generates accurate revenue forecasts using machine learning applied to historical close rates, pipeline velocity, rep behavior, and seasonal patterns — going beyond CRM probability fields to produce reliable predictions.
Before
Forecasts are based on rep-provided probabilities that are often optimistic and inconsistent. Sales leadership has low confidence in the numbers and spends hours reconciling them.
After
The forecast is produced automatically with data-driven probability adjustments. Leadership trusts the numbers. The focus shifts from building the forecast to acting on it.
How the agent works — step by step
- 1
Agent reads full pipeline from CRM: all open opportunities with stage, amount, close date
- 2
Applies historical close rate models by stage, rep, product line, and deal size
- 3
Adjusts for pipeline velocity: deals moving slower than average are down-weighted
- 4
Incorporates seasonal patterns and year-over-year trends from historical data
- 5
Generates three scenarios: commit, best case, and pipeline risk
- 6
Delivers forecast to Sales Leadership channel every Monday with variance to target
- 1
-
Automatically evaluates each sales rep's productivity, conversion rate, pipeline quality, and deal velocity — identifying coaching opportunities and surfacing best practices from top performers.
Before
Sales performance reviews are periodic, manual, and often based on top-line revenue alone. Managers can't tell why some reps convert better than others. Coaching is generic and ineffective.
After
Every manager has a weekly, data-driven view of their team's performance across all key metrics. Coaching is targeted to specific gaps. Best practices from top performers are identified and replicated.
How the agent works — step by step
- 1
Agent pulls sales activity and outcome data from CRM weekly
- 2
Calculates KPIs per rep: activities, conversion rates, average deal size, cycle length
- 3
Compares each rep's metrics against team average and top-performer benchmarks
- 4
Identifies specific gaps: low conversion at a specific stage, high deal slippage, low activity volume
- 5
Generates a weekly performance report per manager with team and individual views
- 6
Flags reps who need coaching and suggests specific focus areas based on the data
- 1
-
Suggests the optimal price for each deal based on historical win/loss data, customer profile, deal size, competitive context, and margin targets — maximizing revenue while maintaining win rate.
Before
Pricing decisions are made by intuition, copying past deals, or applying blanket discounts. The team either leaves money on the table or loses winnable deals on price.
After
Every rep receives a data-driven price recommendation for each deal. Win rates stay high and margins improve. Pricing decisions are consistent and explainable.
How the agent works — step by step
- 1
Deal reaches pricing stage and triggers the agent
- 2
Agent reads deal context: product mix, account tier, deal size, competitive signals
- 3
Analyzes historical deals with similar profiles: what price points won vs. lost
- 4
Applies margin constraints from product and finance systems
- 5
Generates a recommended price range: floor, optimal, and ceiling
- 6
Posts recommendation to rep via Teams with supporting rationale
- 1
-
Analyzes discount patterns across all deals and sales reps to identify excessive discounting, ineffective use of discounts, and opportunities to reduce discount dependency without losing deals.
Before
Discounts are given liberally to close deals. Nobody knows if they're actually necessary. Margin erosion is only visible in finance reports, months after it happened.
After
Sales leadership has a real-time view of discounting patterns. Reps receive coaching on where discounts are losing margin without improving win rate. Overall margin improves.
How the agent works — step by step
- 1
Agent analyzes all closed deals from the past 12 months from CRM and ERP
- 2
Calculates discount rate by rep, product, deal size, and customer segment
- 3
Identifies patterns: where discounts are high, whether they correlate with wins, and where they don't
- 4
Flags deals where discounts were given on deals that would likely have closed anyway
- 5
Generates monthly discount analysis report for Sales VP with rep-level detail
- 6
Sends weekly alert when a deal's discount request exceeds threshold without a clear justification
- 1
-
Identifies lost revenue opportunities, margin erosion, and suboptimal deal structures across your entire sales operation — surfacing patterns that no individual can spot manually.
Before
Revenue is leaking in many small places: missed renewal upsells, underpriced deals, uncaptured fees, and incorrectly applied discounts. None of it is visible until the P&L shows it.
After
Revenue leakage is identified and quantified in real time. The team knows exactly where margin is being lost and can take targeted action to recover it.
How the agent works — step by step
- 1
Agent connects to CRM, ERP, and finance data to analyze the full revenue picture
- 2
Identifies customers with expired contracts still operating without renewal
- 3
Flags deals invoiced below contracted price due to errors or unapproved adjustments
- 4
Detects missed upsell opportunities on accounts with high usage but no expansion
- 5
Quantifies each leakage category in EUR and priority
- 6
Generates monthly revenue leakage report with specific recovery actions per category
- 1
-
Identifies customers at high risk of churn by analyzing product usage, engagement patterns, support history, contract status, and external signals — giving the team time to intervene before the decision is made.
Before
Churn is only confirmed when the customer cancels or doesn't renew. By then, the relationship has deteriorated over months without any visible signal in the system.
After
At-risk customers are flagged 60–90 days before their renewal. The team has time to run a save campaign, address the root cause, and turn the situation around.
How the agent works — step by step
- 1
Agent monitors all active customer accounts continuously
- 2
Reads engagement signals: product usage frequency, feature adoption, login recency
- 3
Analyzes support ticket volume, sentiment, and unresolved issues
- 4
Applies churn prediction model trained on historical churn patterns
- 5
Flags high-risk accounts with a risk score and contributing factors
- 6
Sends weekly churn risk report to CS and Sales teams with account-level actions
- 1
-
Analyzes customer behavior, product usage, and purchase history to identify the best upsell and cross-sell opportunities for each account — delivering timely, relevant suggestions to account managers.
Before
Upsell and cross-sell opportunities are identified by reps when they happen to think about it, or during annual reviews. Most opportunities are missed or spotted too late.
After
Account managers receive a proactive list of expansion opportunities every week. The right products are suggested to the right customers at the right time. Expansion revenue grows.
How the agent works — step by step
- 1
Agent analyzes each customer's current product usage, contract, and purchase history
- 2
Identifies usage patterns that correlate with readiness for a specific upsell or add-on
- 3
Compares account profile against customers who successfully expanded in similar contexts
- 4
Generates a ranked expansion opportunity list per account manager
- 5
Posts weekly opportunity brief to Sales channel with account-specific messaging suggestions
- 6
Updates opportunities in CRM as expansion pipeline for tracking and forecasting
- 1
-
Automatically clusters your customer base into dynamic segments based on value, behavior, industry, growth potential, and engagement — enabling targeted strategies for each cohort.
Before
Customer segmentation is static and based on simple criteria like company size or industry. Marketing campaigns and CS strategies treat very different customers the same way.
After
The customer base is automatically segmented into meaningful clusters. Each segment gets a tailored engagement strategy. Campaign relevance and response rates improve.
How the agent works — step by step
- 1
Agent reads full customer dataset from CRM: firmographics, usage, revenue, engagement history
- 2
Applies clustering model to identify natural groupings across all dimensions
- 3
Labels each segment with a descriptive profile: characteristics, behavior patterns, and strategic value
- 4
Assigns every customer to their primary segment and updates CRM tags automatically
- 5
Generates segmentation report for Sales and Marketing leadership with segment profiles and sizes
- 6
Re-runs monthly to detect customers who have migrated between segments
- 1
-
An AI that answers complex strategic sales questions in natural language — 'Where are we losing the most deals?', 'Which product lines are growing fastest?' — pulling live data and delivering instant, accurate analysis.
Before
Getting answers to strategic sales questions requires pulling data from multiple systems, building a report in Excel, and waiting for an analyst. Decisions are made with stale data or no data.
After
Sales leaders ask questions in natural language and receive instant, data-backed answers with charts and context. Strategic decisions happen in minutes, not days.
How the agent works — step by step
- 1
Sales leader asks a question via Teams in natural language
- 2
Agent identifies the data sources needed: CRM, ERP, marketing automation, finance
- 3
Pulls and aggregates the relevant data from authorized systems
- 4
Runs the appropriate analysis: trend, comparison, cohort, or scenario
- 5
Generates a clear answer with a supporting chart, table, or data summary
- 6
Cites sources and data timestamp so the leader knows the basis of the answer
- 1
Explore other areas
Ready to deploy your first Sales agent?
Talk to an expert. We'll scope the right agent for your processes in one call — no commitment required.