FAQ
This section answers the most common questions about Akkio—what it is, how it works, and how it fits into your agency’s workflows. From technical capabilities to setup and support, get quick clarity on everything you need to know to get started and get value fast.
Model Architecture & AI Design
Model Architecture
Can you walk us through the architecture of how Akkio leverages core model(s)?
Akkio’s platform leverages Retrieval Augmented Generation (RAG) architecture to enhance its core language models, enabling advanced analytics, prediction, and reporting. By utilizing multi-modal capabilities, Akkio operates across a wide range of data sources and formats—including structured ad performance data, campaign reports, and customer-specific documentation—bringing together insights from diverse channels into a cohesive, integrated experience.
Unlike generic language models, Akkio’s RAG framework is embedded directly within agency and client data environments. This architecture allows for scalable, secure analytics that can adapt to each organization’s data landscape, supporting multiple modalities and delivering highly relevant, context-aware insights tailored to specific business needs.
Guardrails are crucial for ensuring the safety, reliability, and appropriateness of both model inputs and outputs, acting as a protective layer for data privacy, content moderation, and preventing harmful or biased outputs.
Do you rely on a single foundational model or a collection of modular models?
Akkio is LLM-agnostic. We work with a suite of proprietary and third-party models, including:
Claude 3.7 Sonnet, Claude 3.5 Sonnet
GPT-4o, GPT-4o-mini
GPT-o1, GPT-o3-mini
Akkio is agnostic to LLM models and can use open-source models if needed. We recommend certain models for certain tasks based on our benchmarking suite.
What are the key innovations or differentiators in your modeling approach?
Akkio delivers an end-to-end AI-native platform purpose-built for advertising—spanning embedded infrastructure, context-aware orchestration, integrated tools, and domain-specific agents. This enables agencies to securely deploy scalable, customizable AI workflows across the entire campaign lifecycle, from data prep to reporting.
Secure, Scalable Infrastructure
Embedded Deployment: Run Akkio securely within your own VPC or cloud, with full IAM and network inheritance.
Cloud- and Model-Agnostic: Compatible with any infrastructure, LLM endpoint, or deployment model—including edge use cases.
Built for Scale: Handles high-volume, multi-source datasets using Spark/Snowpark—no replatforming needed as data complexity grows.
Robust Guardrails: Enterprise-grade controls ensure data privacy, compliance, and secure handling of sensitive information.
Context-Aware Platform Logic
Persistent Context and Execution Control: Retains session goals and data context across interactions to generate coherent, task-aware outputs.
Unified Access and Governance: Manages identity, permissions, and execution via APIs, hooks, and authentication systems.
System Observability: Provides logging, tracing, and performance monitoring to support explainability and debugging.
Integrated Tooling Layer
Automated Data Processing: Cleans, maps, and joins structured data automatically—reducing engineering overhead.
No-Code AutoML + Chat Interface: Anyone can build models, and generate insights using natural language
Domain-Specific Agent Layer
Advertising-Centric Modeling: Built for media use cases—audience segmentation, campaign optimization, forecasting, and measurement.
Agency-Level Customization: Fine-tuned to each agency’s brand tone, data environment, and business logic for more relevant, on-brand outputs.
Explainable, Client-Ready Outputs: Transparent and auditable results that can be used in client reporting or internal decision-making
Model Customization
How do you fine-tune models for specific clients or use cases?
Client/context-specific data ingestion: Fine-tuning starts with gathering client data, including historical records, data schemas, and context unique to the client or use case. Connecting to custom data sources or warehouses ensures the model learns from the right inputs.
Schema and metadata adaptation: The model adapts to client-specific metadata, documentation, usage patterns, and brand needs by ingesting and analyzing schemas, data dictionaries, and sample records. This supports answering data questions accurately in the client’s specific environment.
Custom prompt and model tuning: Prompts, instructions, and outputs are tailored to reflect the client's domain, use case, and brand tone. This might include adjusting language, instructions, and query handling.
Fine-tuning model weights and embeddings: Foundational models (e.g., LLMs) are fine-tuned on client data to better capture nuanced context, and predictive models are trained or retrained on specific outcomes relevant to the client goal.
Iterative feedback: During the deployment/implementation phase, the solution is tested with real client workflows. Feedback is incorporated, and the fine-tuning is adjusted until the desired performance and user experience are achieved.
What parts of the model are adaptable (e.g. prompts, embeddings, retraining mechanisms)
Prompts: Prompts and instructions can be updated, refined, or fine-tuned to reflect new business needs, context, or tone, enabling highly customized user interactions.
Embeddings: User, source, and category embeddings are learned and can adapt over time to changing patterns in the underlying data.
Retraining mechanisms: The entire model can be periodically retrained or fine-tuned as new data is ingested, including both foundational LLMs and task-specific predictive models.
Data environment: The model supports schema changes, new tables, and different data sources, making it adaptable to a variety of enterprise data setups.
Brand and tone: Models can be tuned to agency/client-specific tone and brand style, ensuring outputs are delivered in the right voice.
How often is the model updated or retrained?
For LLMs (large language models), Akkio typically fine-tunes foundational models (such as GPT-4 or Claude) with client-specific data, documentation, and examples during onboarding or after major changes in client requirements. These models are not retrained on a routine schedule but can be re-tuned as needed—for example, if data structures or brand guidelines change, a new round of fine-tuning is conducted to keep responses accurate and on-brand.
For ML models (like lookalike or propensity models), retraining usually happens when new historical data is available, such as after initial onboarding, when clients provide updated datasets, or if feedback indicates model performance needs improvement. The specific retraining frequency can vary, depending on client needs or data refresh cycles, and is often aligned to major data updates or the results of live testing during implementation/deployment.
Model Flexibility
Can different brands or verticals use the same model instance, or is it siloed per client?
Model instances are siloed per client or agency to ensure security, privacy, and precise results matched to each customer’s unique requirements.
Akkio’s platform is designed for strong client-level customization and data segregation. Each agency or client receives a custom-tuned model instance that is adapted to their specific data environment, brand context, and business needs.
The LLM is specifically fine-tuned for the customer’s data, schema, and tone, meaning brands or verticals are not mixed within the same model instance—this supports privacy, relevance, and on-brand outputs for each client.
Agencies can offer a branded LLM experience to their clients, but customizations (such as data schemas and tone) are applied at the agency or client level, not as a shared model across unrelated brands or verticals.
Model Deployment & Configuration
How do you ensure that the accuracy of output is consistent across different models? For example, a model like Mixtral 8X7B is not as capable as GPT-4.1 , so how do you enable the customers to use the model of their choice on their infrastructure?
Akkio's LLM-agnostic platform offers clients model choice (e.g., Mixtral 8x7B, GPT-4.1, Claude) and infrastructure flexibility, optimizing for performance, compliance, and cost. Consistent accuracy is achieved by fine-tuning models to client data with prompt engineering, RAG, and domain-specific controls, enhancing output relevance. Built-in guardrails and benchmarks ensure comparable, accurate outputs across models for critical queries. Regular feedback and performance tracking enable ongoing tuning or model swaps to counteract drift. Clients using their own infrastructure still benefit from Akkio's customization layers and expert guidance. Ultimately, consistent accuracy is a result of model-agnostic infrastructure, intelligent tuning, and continuous evaluation.
How are query SLAs tied to the model choice? Is there any guidance on rate limit configuration, region co-location of model and data, etc.?
Query SLAs are directly impacted by the foundational model you choose: larger, more advanced models generally incur higher latency and lower throughput, while lighter-weight models deliver faster response times and process more requests per second.
For clients deploying Akkio on their own cloud or on-premise infrastructure, rate limits and concurrency settings should be tuned to match both the model’s performance capabilities and the organization’s business requirements.
To minimize network latency and ensure consistent SLAs, co-locate your model hosting environment and data warehouses within the same cloud region (for example, running both in us-east-1 with Snowflake or BigQuery).
Akkio provides support and best-practice guidance for region-aware deployments, including advice on rate limit configuration, horizontal scaling, and other network optimizations for both SaaS and on-prem installs.
For mission-critical, low-latency workloads, Akkio recommends configuring regional affinity, implementing request throttling, setting up active monitoring, and enabling auto-scaling.
Additional tailored advice and tuning is available to help address the specific needs of each use case.
Do you need any specific vector stores?
While Akkio natively leverages the pgvector extension on PostgreSQL for its efficiency, we understand diverse infrastructures. We're fully capable of supporting your preferred vector database or vector embedding model if your requirements call for it.
How do you use data from customers like usage patterns, queries, etc. to fine tune a customer’s experience since this is an on-prem setup offering? Is this data or fine-tuned models / approach also used for other clients?
In an on-prem Akkio setup, all customer usage data (like queries, patterns, and feedback) stays within the client’s secure environment. This data can be used locally to fine-tune and personalize the model for that specific customer, improving relevance, tone, and accuracy based on their own workflows and context. Neither the customer’s data nor any fine-tuned models or derived approaches are shared with, or used for, any other clients—each instance is siloed to ensure privacy and customization.
Safety, Guardrails & Responsible AI
What guardrails are in place to avoid model drift or bias?
Akkio minimizes off-topic or unsupported responses, preventing bias, through prompt and retrieval guardrails. Models are continuously fine-tuned on live client data, metadata, and business context to maintain relevance and prevent drift. Context-specific fine-tuning and regular output review ensure the model reflects each agency’s tone and standards, filtering irrelevant or biased outputs. Retrieval-Augmented Generation (RAG) pipelines ensure responses use up-to-date, verified data, addressing drift and bias. Post-deployment, model performance is monitored, and user feedback is integrated into retraining and prompt refinement cycles for continuous correction of drift or bias. Finally, only client-specific data is used in training and inference, preventing pollution from unrelated business contexts.
What are your measures for responsible AI?
Akkio takes several measures to support responsible AI:
Integrations, security controls, and deployment in on-prem environments are available for maximum control over sensitive data and compliance with organizational and legal standards
The platform includes guardrails to manage ethical risks, prevent off-topic responses, and limit unsupported recommendations.
Models are fine-tuned to each client’s data environment and business context to ensure relevance, accuracy, and on-brand results while maintaining strong data privacy.
Data and models are siloed per client, never mixed or reused across customers, supporting compliance and confidentiality.
Continuous feedback loops: Users can review and flag AI outputs via thumbs up/down or comments (HiTL), directly improving model accuracy and alignment with expectations.
Explainable and auditable AI outputs: Akkio enhances transparency by showing how AI decisions are made, including explanations or underlying code, allowing users to understand, verify, and audit results for trust and compliance
How does your model consume external signals such as delay in data-processing, so that your model can respond with appropriate response, rather than hallucinations?
Akkio's system can generate and execute code directly in your data warehouse. This means that for data analysis or retrieval tasks, the model doesn't just guess; it can write and run queries, ensuring any results are logically and functionally correct based on the most current data available. This tight integration with the warehouse significantly reduces the chance of hallucinations.
Data Layers & Inputs
Data Sources & Formats
What types of data are required to make the solution work effectively?
For effective analysis, provide comprehensive, consistent, and clean historical and current marketing or business data, including ad platform, CRM, web analytics, and sales data. The addition of 1st party data, demographic data, and enrichment data further enhances the depth and accuracy of insights. Ideally, this should span 2-5 years for seasonality and trends, be granular (daily/weekly preferred), and clearly structured with defined metrics and dimensions. Access to data dictionaries and schemas enhances model customization. More connected datasets across platforms and campaigns lead to more accurate and actionable insights.
How many data layers are integrated into a single client/brand’s Akkio instance? How are they prioritized?
A single client or brand’s Akkio instance can integrate multiple data layers, including first-party (internal transaction, CRM, web analytics), second-party (partner data), and third-party (external platforms like Google Ads, Facebook Ads, surveys, or enrichment sources). These data layers are merged, joined, and orchestrated within Akkio based on the client’s use case and data architecture—often across many tables and data sources at once. Akkio’s tools map, clean, and relate these data layers for optimal analysis and AI modeling.
Prioritization depends on the business need, data quality, and relevance. Typically, core first-party operational and performance data is prioritized, followed by high-impact second- and third-party inputs to enrich models and drive deeper insights or predictions. Agencies can further configure which datasets are primary for each use case or report through the UI or integration settings.
What real-time data, if any, is ingested and used in optimization?
Akkio seamlessly integrates with your existing cloud data warehouse (like BigQuery or Snowflake), leveraging your available data for analysis and optimization. This ensures real-time insights and updates are always based on your most current information.
The platform can run predictive models, generate suggestions, and adapt optimizations based on the latest available data in your connected systems. This process supports high-frequency campaign assessments and rapid deployment of insights, but is not designed for millisecond-level or sub-second real-time bidding or execution.
Typical real-time data flows include updated ad metrics, spend, impressions, conversions, and CRM activity—anything made freshly available via your connected platforms or data warehouses can be leveraged for ongoing optimization
Do you expect the customer’s data to be available in a certain format? Do you need metadata associated with the raw data?
Akkio flexibly ingests data from various sources and formats, including data warehouses, ad platforms, and files, provided the structure is consistent and key fields are defined. While no rigid schema is required, clear, organized tables with well-labeled columns for metrics and dimensions are preferred, along with granular data (daily or weekly).
Metadata (column headers, data types, dictionaries, summary statistics) is highly beneficial for automated ingestion, mapping, and accurate model outputs. Akkio leverages existing metadata or generates it as needed. Data documentation and context are recommended for best results, though Akkio can assist in creating these during onboarding.
Data Ownership & Access
How is customer data kept siloed and secure?
Akkio keeps each customer’s data siloed and secure by supporting deployment in the client’s own cloud or on-prem environment, ensuring data never leaves that infrastructure unless authorized. All data is encrypted at rest and in transit, with multi-factor authentication, role-based access controls, and rigorous security protocols. The platform is SOC 2 Type 2 certified, follows GDPR and HIPAA compliance standards, and employs regular penetration testing, vulnerability scanning, and structured data retention and deletion policies.
Client data is never shared or used for general model training and models are customized only for the client’s data and environment. Continuous security monitoring, dedicated incident response, and a comprehensive privacy framework further ensure each customer’s information remains fully isolated and protected.
What data does your platform store long-term vs. process transiently?
Most sensitive customer data—like raw datasets, queries, and analytics results—is processed transiently within secure environments and is not stored longer than needed for each session, calculation, or report generation. In on-prem or customer-hosted deployments, raw data remains within the client’s infrastructure and never persists on Akkio’s servers. Customer data is never used for general model training or shared with others, ensuring privacy and compliance.
Akkio primarily stores data necessary for ongoing operations, such as metadata about connected data sources, user settings, configuration details, data transformation templates, model parameters, and saved reports or dashboards. These are retained long-term to support user experience and platform continuity.
Do you use any synthetic data generation for training?
Akkio models are trained exclusively on real-world data, not synthetic data, ensuring the highest fidelity and relevance. Clients remain in control, with the flexibility to use synthetic data if it aligns with their specific needs.
Performance & Accuracy Over Time
Learning & Accuracy
How does the solution improve over time? What feedback loops exist?
Akkio’s solution improves over time through a combination of user feedback, continuous data ingestion, and repeated model fine-tuning:
Users can provide feedback directly within the platform, and this input is used to identify areas for improvement, update prompts, and refine the user experience.
The system continuously ingests updated client data, enabling models to be retrained as the underlying business context and datasets evolve, keeping outputs relevant and accurate.
Client-specific tuning incorporates support tickets, documentation, and historic interactions, so the model responds more accurately in the client’s tone and context over time.
New features and product improvements are also guided by customer feedback reports, product usage analytics, and observations from support and customer success teams.
Automated monitoring catches errors or inconsistencies, prompting product and engineering teams to address issues and further improve the solution.
How do you validate model accuracy against real-world outcomes?
Akkio validates model accuracy by comparing predictions to real-world outcomes using historical client data (e.g., campaign performance, sales). The platform supports A/B testing to align model predictions with actual business impacts. Models are retrained and evaluated on unseen datasets using metrics like accuracy and C-index before deployment. Continuous integration benchmarks model performance against competitors, and clients can interactively review outputs for an additional real-world check.
What benchmarks do you use for model performance evaluation?
Akkio benchmarks model performance using use-case tailored metrics, including data accuracy, business KPIs (like ROI or conversion rates), and predictive model statistics (e.g., accuracy, C-index, lift). Success is measured by how closely model outputs align with real-world results and expected outcomes.
Quantitative benchmarks involve evaluating correct outputs on sample datasets, consistency across various dataset sizes, and side-by-side comparisons against alternative models or manual calculations. Deployments define upfront metrics and success criteria, validated by both manual spot checks and automated testing. Benchmarking can also include workflow efficiency and qualitative measures, especially for reporting
Observability & Transparency
Monitoring & Debugging
How does Akkio ensure robust model performance and behavior in production?
Akkio ensures robust model performance and behavior in production through a comprehensive approach: we define success metrics, validate data accuracy, and involve stakeholders in ongoing testing during implementation. In production, we leverage a combination of internal benchmarking tools and Datadog for real-time observability, including logs, tracing, and alerting, to rapidly identify and resolve issues. This allows for continuous monitoring of output accuracy, anomaly detection, and the application of product-specific guardrails, ensuring transparent, auditable, and reliable model performance.
Are prediction logs or decision trees visible to clients?
Clients can view predictive results and insights—such as key drivers, explanatory charts, and the reasoning behind model predictions—through Akkio’s reporting and dashboard features. However, raw prediction logs or detailed decision tree structures are not directly exposed by default. Instead, clients access high-level outputs, explanations, and shareable visualizations, making results transparent and actionable but not overwhelming with underlying technical details. Reports and prediction outcomes can also be downloaded or added for sharing as needed
Do you give insights about the usage patterns per user? We would want to identify our champion users who are using the platform on day-to-day basis
Akkio can provide insights into user-level usage patterns, allowing you to track and identify champion users who are most engaged and active on the platform day-to-day. These analytics enable organizations to recognize key users, understand adoption, and optimize training or support for those driving the most value.
Transparency
Can clients audit or review how decisions are made?
Yes, clients can audit and review how decisions are made in several ways. Akkio provides access to high-level outputs, explanatory charts, and the reasoning behind predictions through dashboards and shareable reports, making model results transparent and actionable. Clients can also view determining factors, driver analyses, and—in certain advanced use cases—can interrogate some models to see details like feature importances or Shapley Values for predictions. While raw logs or tree structures are not exposed by default, the end-to-end workflow and results remain visible and reviewable, supporting transparency and trust in the decision process.
Do you offer explainability tools or dashboards?
Yes, Akkio offers explainability tools and interactive dashboards designed to make model insights clear and actionable. Users can automatically generate dashboards that summarize key campaign metrics, surface drivers and trends, highlight best/worst performers, and present AI-powered recommendations. Visual explanations accompany each chat output that shows the specific AI interpretation and approach on each response, as well as the generated code that the prompt query is run against. You can drill down to understand the reasons behind model outputs, with chart insight summaries, driver lists, pacing-risk callouts, and natural language chat to explore results. These tools help users move beyond “what happened” to “why” and “what to do next,” making campaign optimization more transparent and effective.
Cost vs. Accuracy Trade-Offs
Do you offer tunable options for clients who want faster execution vs. higher accuracy?
Yes, Akkio is LLM-agnostic and allows clients to select from different foundational models and configurations, enabling a tradeoff between speed and accuracy. For example, lighter models like GPT-4o-mini or similar variants can be used for faster responses, while larger models provide higher accuracy for complex tasks. Clients can adjust these settings based on their specific requirements for execution speed versus analytical depth.
How do you manage cost when selecting which model(s) or compute infrastructure to use?
Akkio manages cost by allowing clients to select from a range of LLM sizes and compute configurations, matching model complexity and infrastructure to their specific budget and performance needs. Lightweight models and efficient batch processing lower resource usage and inference costs, while more powerful models are available for scenarios that require higher accuracy or advanced analytics. Clients can run models on their own cloud or on-prem infrastructure, optimizing deployment based on their existing resources and cost structure, and tuning factors like concurrency, rate limits, and batch sizes to further control expenses.
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