AI Chatbot
How a US Performance Marketing Agency Validated a Custom AI Chatbot for Campaign Reporting and Client Q&A
A US-based performance marketing agency wanted to automate routine reporting work, reduce the time it took to extract campaign insights, and improve how clients accessed campaign information. Rather than rolling out AI across the business all at once, the agency chose a phased proof-of-concept approach to test whether a custom AI chatbot could support both internal workflows and client-facing conversations without compromising reliability.
The result was a multi-phase proof of concept (PoC) that validated the operational value of a custom AI assistant for campaign analysis, reporting, and real-time client communication — while also clarifying technical requirements for a production rollout.
This case study is relevant for
-
marketing agencies exploring a custom internal and client-facing AI chatbot / AI assistant
-
service businesses looking to automate campaign reporting and repetitive analysis
-
agencies wanting faster access to Meta/Facebook Ads insights
-
companies evaluating AI projects through proof of concept before full implementation
-
organizations where AI outputs must be grounded in business data
About the project
Key challenge
The agency’s team was spending too much time on repetitive reporting, campaign analysis, and recurring client questions. They needed a reliable AI-powered assistant that could surface campaign insights quickly, support internal workflows, and eventually serve as a client-facing interface.
Complexity factors
The marketing agency had fragmented campaign data with no unified creative-to-performance mapping. Meanwhile, their LLM required custom context training on client-specific KPIs, naming conventions, and account structures, and they faced real-time processing constraints for interactive conversational use.
Core systems involved
Meta/Facebook campaign data, internal reporting workflows, chat interface, context management layer, analytics processing, campaign data normalization and mapping, internal server deployment roadmap.
Key results
-
01
Identified 15–20 hours per week of repetitive reporting work suitable for automation
-
02
Validated a phased AI adoption model instead of rushing into full-scale implementation
-
03
Built and tested two proof-of-concept versions of a custom AI chatbot
-
04
Improved usability in PoC-2 by removing manual login friction from the user flow
-
05
Validated real-time conversational access to campaign data for internal and client-facing use cases
-
06
Defined a roadmap for production deployment on internal infrastructure
About the client
The client is a US-based performance marketing agency that manages campaign performance, reporting, and client communication across multiple accounts. As the agency grew, routine work began to consume too much of the team’s time. Account managers were repeatedly pulling campaign data, preparing standard reports, and answering similar client questions across email and calls.
The leadership team saw AI as a possible way to reduce that operational load, but they didn’t want to deploy it blindly. The goal was not to add AI for optics. Instead, they wanted to validate whether a custom chatbot could be a practical tool for accelerating insight delivery and improving transparency while working reliably enough for both internal and client-facing use.
About the challenges we faced
Four constraints made this initiative more complex than a standard chatbot build.
-
Routine work was operationally
expensive
The client’s team was spending significant time every week on repetitive campaign analysis, data extraction, and reporting. This time was dedicated to maintaining basic visibility for internal teams and clients, not creating new strategic value.
-
Insights were too slow
to access
Campaign data existed, but extracting useful answers required digging through multiple systems and manually interpreting performance context. The agency needed a faster path from raw campaign data to usable insights.
-
Client communication was
too dependent on email and calls
Clients often had to wait for someone on the agency side to answer questions about campaign performance. The agency wanted a more immediate, interactive way for clients to access campaign information.
-
Reliability mattered from
day one
Because the solution was intended for internal use and client-facing demonstrations, tolerance for failure was low. The system could not return incorrect data, lose context, or break under normal use.
Technical challenges discovered
During discovery, we identified several technical blockers that had to be resolved before a chatbot could deliver reliable answers.
Technical challenges discovered
During discovery, we identified several technical blockers that had to be resolved before a chatbot could deliver reliable answers.
-
01
Creative-to-performance gaps
The agency did not have a fully unified structure linking creative assets to campaign performance data.
-
02
Context management complexity
The AI needed to understand campaign structure, client-specific KPIs, naming conventions, and agency terminology.
-
03
Real-time response requirements
Campaign data had to be processed fast enough to support interactive conversations rather than delayed reporting workflows.
-
04
Scalability concerns
The solution needed to handle multiple clients, multiple campaigns, and growing usage without performance degradation.
What we considered before recommending a phased AI PoC
The agency’s interest in AI was clear. What we needed to validate was the implementation path.
A full production build from the start would have increased delivery risk before the team had evidence about usability, accuracy, and operational fit. At the same time, a lightweight chatbot with no campaign context would not have solved the real problem.
While it was clear that AI was relevant for the use case, our task was to validate its value to avoid overcommitting.
What we ruled out — and why
-
Immediate full-scale
rolloutGoing straight into production would have meant building infrastructure, workflows, and client-facing logic before validating whether the chatbot actually improved daily operations. We ruled this out because the agency wanted evidence before committing to full adoption.
-
Generic off-the-shelf
chatbot setupA generic chatbot could answer broad questions, but it would not understand campaign structures, account logic, or client-specific performance context. We ruled out setting up a generic solution because the client’s problem required grounded, business-specific answers rather than general AI responses.
-
Dashboard-only
optimizationImproving reporting dashboards alone could have made analytics easier to read, but it would not have solved the need for conversational access, real-time client Q&A, or workflow automation. We ruled out dashboard-only optimization because the agency wanted an interactive assistant, not just a cleaner reporting interface.
What we chose — and what it required from the client
We recommended a phased proof-of-concept model: discovery first, then two iterative PoC stages, followed by a production integration roadmap.
This approach required the client to stay closely involved in workshops, testing, and feedback cycles. It also required access to real campaign data, internal process knowledge, and operational input from the team using the system.
Moreover, our recommendation was about balanced speed with control. It gave the client a way to validate business value, user behavior, and technical feasibility before committing to a full rollout.
About the solution we delivered
Phase 1
Research and planning
-
Strategic alignment
We conducted intensive workshops with the client’s in-house team to understand their current workflows, pain points, and success metrics. These sessions revealed that the team was spending approximately 15–20 hours per week on routine reporting tasks that could be automated.
-
Technical discovery
Our team analyzed the client’s existing technology stack, including their Facebook Ads accounts, reporting tools, and client communication systems. We identified integration points and potential data sources while also mapping out the user journey for both internal team members and clients.
-
Gap analysis
We discovered several data gaps that needed to be addressed, including missing creative mapping tables that linked ad creatives to performance metrics. Through feature prioritization, we defined an MVP that delivers essential chat functionality with campaign data while reserving advanced features like predictive analytics and automated optimization suggestions for the full-scale product.
-
Business model evaluation
We conducted a comprehensive analysis, comparing subscription-based delivery versus custom implementation and considering factors such as development costs, maintenance requirements, scalability potential, and revenue impact.
Phase 2
PoC-1
-
Infrastructure setup
During this phase, we built the first prototype with a scalable architecture designed to handle multiple concurrent users and API requests. The system included proper logging, monitoring, and error handling from the start.
-
AI integration
Next, we integrated a fine-tuned LLM with custom context specifically trained on terminology, campaign structures, and the company’s internal processes. The AI was designed to understand queries such as: How did the Casper campaign perform last week? and Show me the top performing creatives for Blue Nile.
-
Initial validation
We conducted extensive testing with real campaign data to validate the AI’s ability to process complex queries and provide accurate insights. We focused on common use cases like campaign performance summaries, creative performance analysis, and budget allocation recommendations.
-
Feedback collection
We implemented structured feedback mechanisms to gather insights on usability, accuracy, and feature requests from the client’s team during the testing phase.
Phase 3
PoC-2
-
Enhanced user experience
For the second proof of concept, we completely redesigned the interface based on feedback from PoC-1. The result was a more intuitive chat experience that eliminated the need for manual Google/Meta logins. Users could now authenticate once and access all their campaign data seamlessly.
-
Advanced analytics
During this phase, we also introduced sophisticated reporting capabilities, including automated performance summaries, trend analysis, and anomaly detection. The system could now identify underperforming campaigns and suggest optimization strategies.
-
Improved reliability
We strengthened error handling, data validation, and fallback mechanisms to ensure the system could function reliably in client demo environments. This included comprehensive testing with edge cases and unusual data scenarios.
-
Real-time insights
We implemented faster time-to-insight reporting that could process and analyze campaign data in real time, providing immediate responses to user queries about current campaign performance.
-
Client-ready features
We added features specifically designed for client interactions, including customizable dashboards, automated performance alerts, and the ability to schedule regular campaign updates.
-
Phase 4
Integration roadmap to maximize effectiveness
-
Learning consolidation
In this final phase, we compiled comprehensive documentation of learnings from both PoC phases, including technical insights, user feedback, performance metrics, and areas identified for improvement.
-
Production planning
We outlined a detailed roadmap for scaling the PoC into a production-ready tool, including infrastructure requirements, security considerations, and deployment strategies.
-
Integrating the solution
Lastly, we initiated integration of the AI chatbot into the internal servers of our client to ensure stable performance, secure data handling, and seamless alignment with the agency’s existing marketing infrastructure. This phase is currently in progress, laying the foundation for a full production rollout.
How the chatbot works
-
01
User question
-
02
Chat interface
-
03
Authentication
and access control -
04
Campaign data connectors
(Meta/Facebook Ads data + internal reporting sources)
-
05
Data normalization and mapping layer
(creative mapping, campaign
structures, KPI logic) -
06
Context builder
(client-specific terminology, account logic, reporting context)
-
07
LLM processing layer
-
08
Validated response layer
(performance summary, insight, trend, recommendation, fallback)
-
09
Logs, monitoring,
and feedback collection
One friction point that required active management
The biggest issue uncovered early involved the data relationship layer beneath the model.
Because campaign performance data and creative asset mapping were not fully standardized, the chatbot could not always rely on a clean one-to-one structure when answering performance questions. Resolving that required extra work on normalization and contextual mapping before the AI layer could be trusted consistently.
This did not invalidate the PoC, but it changed the implementation logic: the solution had to be treated as a data and context system, not just a chatbot interface.
Lessons learned
for agencies evaluating
AI chatbots
This case highlights three lessons for agencies exploring AI workflow automation.
Lessons learned
for agencies evaluating
AI chatbots
This case highlights three lessons for agencies exploring AI workflow automation.
-
01
A useful AI chatbot needs a structured business context
If campaign naming, creative mapping, KPI definitions, and account logic are inconsistent, the AI will reflect that inconsistency. The quality of answers depends on the quality of context.
-
02
AI adoption works better
when it’s phasedFor service businesses, a phased PoC is often a better path than an immediate full rollout. It reduces risk, reveals technical gaps early, and gives teams evidence before operationalizing AI more broadly.
-
03
Client-facing AI requires a higher
standard of reliabilityAn internal assistant can tolerate rough edges during testing. A client-facing assistant cannot. Validation, fallback logic, and data integrity need to be built into the system from the beginning.
A common mistake is to treat an AI chatbot as a UX feature. In practice, it is an operational system that depends on data quality, context logic, and workflow design.
Results we achieved
-
01
Strategic
-
Proved that a phased PoC model is a viable path to AI adoption for service agencies, reducing implementation risk without slowing down validation
-
Confirmed that a single custom AI assistant could serve both internal operations and client-facing workflows within the same architecture
-
Delivered a production roadmap grounded in real usage data
-
-
02
Operational
-
Shifted approximately 50% of weekly reporting workload to the chatbot, freeing account managers for higher-value work
-
Reduced time to insight for campaign performance questions
-
Eliminated recurring client Q&A bottlenecks by enabling direct, immediate access to campaign data through chat
-
-
03
Technical
-
Validated the chatbot as client-ready: tested against edge cases, demo environments, and real campaign data before making it available to clients
-
Implemented a scalable prototype architecture with logging, monitoring, and error handling
-
Integrated an LLM with campaign-specific context handling
-
Improved reliability through stronger validation and fallback mechanisms
-
Initiated internal server integration planning for production deployment
-
Need a custom internal tool to reduce manual work, improve reporting, or simplify client-facing workflows?
Talk to our solution architectAbout the capabilities demonstrated
-
AI discovery
-
Phased PoC delivery
-
Custom AI chatbot development
-
LLM integration
-
Data normalization
-
Conversational analytics
-
Workflow automation
-
Interface redesign
-
Production readiness planning
-
Key services behind this engagement
-
Learn moreCustom software development
We build tailored software products and internal business tools for companies whose workflows, data structure, or operational requirements go beyond what standard platforms support.
-
Learn moreDigital asset management consulting
We help businesses improve how digital assets are structured, stored, governed, and used across teams — making content easier to manage, find, and activate in day-to-day operations.
-
Learn moreWeb development services
We create web-based systems, dashboards, and portals that support business processes, improve the user experience, and integrate with the tools teams already use.
Other relevant case studies
Salesforce team augmentation for a digital agency
Staffing senior SFCC developers and streamlining delivery for a US-based full-service digital agency
-
Staffing top-tier Salesforce developers in 8 days with zero hiring delays
-
Improving architectural design, backend automation, and Salesforce interface usability
-
Enabling seamless integration with external systems through advanced backend development
Scaling B2B and B2C operations for a Canadian retailer
Connecting a large Canadian retailer’s B2C and B2B platforms on a single scalable Magento 2 foundation
-
Integrating B2B and B2C platforms with the client’s ERP for real-time inventory and pricing updates
-
Building a custom API connector to modernize ERP data exchange formats
-
Achieving a 27% increase in productivity and a 36% increase in business process automation
Print on demand platform rescue for a Czech printing business
Rebuilding a fragile legacy platform into a scalable, automated order processing system
-
Reducing manual tasks by 70% through business process automation and PrintQ integration
-
Speeding up order processing by 60%
-
Overhauling documentation management, payment options, and international transaction handling