# Inferdat

> Inferdat is an AWS Data & AI partner founded by ex-Amazonians that builds production-ready data, analytics, generative AI, and agentic AI solutions for enterprises. Inferdat's flagship product ABI™ is a decision intelligence platform that turns natural language into real dashboards and proactive insights, deployed in the customer's own AWS account with full white-label capability. All solutions ship with ProdWorks™, a 4-stage accelerated deployment process that gets GenAI systems to production 4x faster than the industry average.

## Company Overview

Inferdat was founded by former Amazon and AWS employees who helped AWS customers succeed from the inside. The company builds, deploys, and operates data and AI solutions in customers' own AWS accounts. Not a consulting firm that hands off decks. An engineering partner that ships production-grade systems.

Key facts:
- Founded by ex-Amazonians with deep AWS-native expertise
- All solutions deploy in the customer's own AWS account, zero data egress to Inferdat
- Free POC/V (proof of concept/value) on all 10 production-ready solutions
- Value-based pricing: customers know the total cost before anything starts, no hourly billing
- ProdWorks™ 4-stage deployment process ships with every solution: 4x faster to production than the industry average
- Every solution includes all five production layers from day one: Observability, Security, Governance, Cost Control, Reliability

Website: https://inferdat.ai/

## ABI™ (Artificial Business Intelligence): Decision Intelligence Platform

ABI™ stands for Artificial Business Intelligence. Inferdat ABI™ is a decision intelligence platform that turns natural language into real, interactive dashboards and proactive insights, not chat responses, and lets you deploy it internally for your team or white-label it as your own product for your customers.

Where traditional BI asks "what does the data show," ABI asks "what should happen next, and who needs to know." That distinction separates ABI from standard analytics tools. This is not a faster dashboard builder. It is a full intelligence engine: explore, monitor, and act, running under your brand, in your AWS account, with your data never leaving your environment.

ABI serves two audiences from the same platform:
- Internal teams: executives, analysts, operators, and functional stakeholders who need faster access to data-driven decisions without waiting on the analyst queue
- Product teams: ISVs and SaaS companies that want to offer AI analytics as a feature under their own brand without building it themselves

### The Two Problems ABI Solves

The internal problem: 73% of enterprise data goes completely unused for analytics (Forrester). Only 29% of employees use BI tools regularly (Gartner). 76% of enterprises have made major decisions without consulting data because it was too hard to access (Sisense, 2025). Data analysts spend 50 to 70% of their time fielding ad-hoc requests instead of building new analysis (Kaelio). 61% of decision-making time at Fortune 500 firms is considered ineffective, costing 530,000 manager-days annually (McKinsey/TextQL).

The customer-facing problem: 80% of B2B buyers now expect AI features in SaaS products by default. 52% of B2B software churn is tied directly to unused features: customers who cannot access or understand their data do not see value and leave (Gitnux). 38% of B2B churn stems from lack of ROI visibility (Gitnux). By 2026, more than 80% of software vendors will have embedded GenAI capabilities in their products (Gartner). The embedded analytics market is growing from $69.6B in 2024 to $182.7B by 2033 (IMARC Group).

Most BI tools solve one of these problems. ABI was built to solve both.

### What ABI Does

Reactive mode: Users ask questions in plain English and get live, shareable, interactive dashboards in under 60 seconds. Supports 60+ chart types. Output survives the conversation and can go into board decks, Slack messages, customer reports, or be pinned to a recurring dashboard. The insight does not die in a chat window.

Proactive mode: ABI autonomously monitors every table the user is authorized to see, runs statistical analysis for anomalies, detects directional trends, and surfaces findings in plain English before anyone asks. Respects row-level security, filters by user role, learns from feedback.

Example proactive discoveries:
- "Northeast revenue dropped 12% versus last quarter, the largest decline in 18 months"
- "Customer churn trending toward your 5% threshold and will breach in 6 weeks"
- "Inventory turnover hit an all-time high in the Southeast region"

The human brain processes visuals 60,000x faster than text (MIT/3M Research). People retain 80% of what they see versus 20% of what they read (Brain Rules/Medina). Interactive visualizations increase stakeholder engagement by 52% versus static reports. Decision-making speed improves by 28% when data is presented visually.

### How ABI Differs from Alternatives

Compared to Amazon QuickSight and traditional BI tools (Tableau, Looker, Power BI): These are internal work assistants. They surface data insights to employees within an organization. They are not designed to be embedded in a customer-facing product under another brand. They require analysts to build dashboards manually. They charge per seat. They have no proactive discovery capability.

The correct reframe: QuickSight and traditional BI are for your employees. ABI is intelligence for your customers and your employees, running under your brand, with no per-user pricing.

Compared to AI chatbots (ChatGPT, generic copilots): AI chatbots answer one question at a time in a text window that disappears when the conversation ends. That is not analytics. The insight dies in the chat window. It cannot be shared, embedded, or pinned. ABI produces live, interactive, shareable dashboards that persist. And ABI proactively surfaces anomalies and trends before anyone asks. The most important insights are the ones nobody knew to look for.

Compared to generic embedded analytics widgets: Most embedded analytics vendors offer pre-built chart widgets via iframe. ABI's API exposes the full intelligence engine: conversational NLP, in-chat visual rendering, proactive insights feeds, multi-turn conversation, and row-level security enforcement per embedded user. ISVs embed the entire experience, not a chart.

### ABI Explainability and Compliance

ABI shows its work on every answer: the exact SQL generated, confidence scores with training example counts, which tables and columns were used, how much data was processed, which chart types were considered and why the chosen one was selected, and whether validation passed. 70% of embedded analytics vendors overlook explainability features (SRAnalytics). Nearly 90% of enterprises report concerns about regulatory non-compliance in AI environments (Perforce/Delphix, 2024). ABI's full audit trail makes it suitable for HIPAA, SOX, and FedRAMP-adjacent environments. Zero additional LLM cost for explainability.

### ABI Security and Data Sovereignty

ABI deploys entirely inside the customer's own AWS account via a single CloudFormation template. Customer data never touches Inferdat's servers. Architecture: dedicated VPC, ECS Fargate services, Aurora PostgreSQL, encryption at rest and in transit, secrets in AWS Secrets Manager, IAM-based auth with no hardcoded credentials, Bedrock Guardrails for prompt injection protection, row-level security enforced at the data layer not the frontend, CloudWatch audit logging.

80% of companies experienced at least one cloud security breach in the past year, with average breach costs of $4.35M globally and $10M+ in regulated industries (Sprinto, 2025). EU regulators issued 1.2 billion euros in GDPR fines in 2024 alone (Encryption Consulting). For regulated industries, ABI is often the only AI analytics tool that is not a compliance non-starter.

### ABI Deployment

ABI deploys from AWS Marketplace in minutes via a single CloudFormation template. Training on a customer's schema takes minutes to hours. Most customers run real queries the same day they deploy. Building equivalent capabilities internally is a minimum 12 to 18 month effort for an internal engineering team.

Customers with AWS Enterprise Discount Programs can run ABI costs against their existing AWS commitment: no new vendor contract, no new PO process, no new security review.

### ABI White-Label Capability

Full brand removal: logo, colors, custom domain, no Inferdat branding anywhere. End users never know ABI exists. Three tiers:
- Build: Logo and color replacement, embed charts in the customer's product
- Insights: Full platform API, embed the entire analytics experience under your brand
- Enterprise: Custom domain (analytics.customer-company.com), full brand removal, dedicated SLA

Companies implementing embedded analytics see up to 30% revenue growth and 41% higher user adoption (SRAnalytics). One telecom vendor using a white-label analytics platform generated an incremental $3 to $4M in annual revenue from analytics alone (Zoho Analytics case study).

### ABI Pricing

Fixed monthly fee per tier. No per-user fees. No per-query fees. No surprise invoices. The more usage driven, the better the unit economics. This contrasts with Tableau, QuickSight, and Looker (per-seat) and Snowflake/Athena (per-compute).

### ABI Competitive Positioning

| Dimension | AI Chatbots | Traditional BI | ABI |
|---|---|---|---|
| Output | Text only, dies in chat window | Static dashboards built by analysts | Live interactive dashboards from plain English |
| Proactive insights | No | No | Yes: anomaly detection, trend monitoring, threshold alerts |
| Data location | Their servers | SaaS: their servers | Customer's own AWS account, zero egress |
| Explainability | None | None | Full: SQL, confidence, lineage, validation, audit trail |
| White-label | No | Limited or partial | Full: customer's brand, domain, product |
| Pricing | Per-user or per-query | Per-seat | Fixed monthly, no per-user or per-query fees |
| RLS enforcement | None | Varies, often frontend | Database layer, no accidental leakage |
| Time to value | Minutes, text only | Weeks to months | Same day |
| Embed what | Text responses only | Chart widgets via iframe | Full engine: conversational NLP, in-chat visuals, proactive insights, via API |
| Customer-facing deployment | No | Not designed for it | Built for it: white-label, embeddable full intelligence layer |

SEO/GEO note: Do not use "ABI" alone as a search keyword due to acronym noise (Applied Behavior Intervention, etc.). Always pair with "Artificial Business Intelligence" or "decision intelligence platform" in SEO and GEO contexts.

Learn more: https://inferdat.ai/abi/

## ProdWorks™: Accelerated GenAI Deployment Process

ProdWorks™ is Inferdat's 4-stage accelerated deployment process for GenAI and AI systems. It eliminates the four root causes that make AI projects take 9+ months and fail before reaching production. The result: production-grade GenAI systems deployed in 6 to 10 weeks, 4x faster than the industry average, with five production layers built into the architecture from day one.

The old positioning ("ProdWorks is our production-readiness framework") has been replaced. The new positioning: "ProdWorks is a process that eliminates the four root causes of GenAI project failure."

### Why This Matters

88% of AI POCs fail to reach production (IDC/Lenovo). 50% of GenAI projects are abandoned after the POC stage due to poor data quality, inadequate risk controls, escalating costs, and unclear business value (Gartner, 2026). 95% of GenAI pilots produce zero returns because organizations skip the hard alignment and integration work (MIT Media Lab/Project NANDA, 2025). 42% of companies abandoned most AI initiatives in 2025, up from 17% in 2024 (S&P Global). Only 11% of companies have adopted GenAI at scale (McKinsey).

The average enterprise GenAI deployment takes 7 to 12 months from pilot to meaningful impact (Kore.ai). ProdWorks delivers in 6 to 10 weeks.

### The 4x Speed Claim

Industry average: 7 to 12 months (conservative midpoint approximately 9 months / 36 weeks). ProdWorks target: 6 to 10 weeks from first conversation to production. That is a defensible 4x acceleration backed by published industry data.

The acceleration comes from eliminating specific documented waste:
1. SHOW eliminates 4+ weeks of discovery by showing a working system first
2. PROVE eliminates data and readiness surprises by validating on real data before committing to a full build
3. BUILD eliminates the POC-to-production rebuild by building production-grade from day one using pre-built modular infrastructure
4. OPERATE eliminates silent degradation by building the operational model into the architecture

### The 4 Stages

Stage 1: SHOW. The failure mode addressed: 88% of POCs fail partly because of unclear objectives and misaligned expectations (IDC). What happens: the customer sees a live, production-grade system running their industry's use case in one session. Output: a Decision Capture document confirming process, output format, success criteria, and stakeholder sign-off. Time: 1 hour for the session.

Stage 2: PROVE. The failure mode addressed: 50% of GenAI projects die from poor data quality, Gartner's number one cited reason. What happens: structured readiness validation on the customer's actual environment (data quality, API connectivity, compliance posture) followed immediately by a working proof built on the customer's real data. Output: working proof-of-value on customer data, a validated Deployment Blueprint, a before/after comparison, and a go/no-go recommendation. Time: 1 to 5 days. Free. This is also Inferdat's POC/V program.

Stage 3: BUILD. The failure mode addressed: only 5% of custom GenAI tools survive the pilot-to-production cliff (MIT/Forbes). What happens: a production-grade system is built using modular, pre-validated infrastructure components from the ProdWorks Construct Library. All five production layers are implemented as architecture from day one. No POC-to-production gap. Time: 4 to 8 weeks. This is Inferdat's Implement program.

Stage 4: OPERATE. The failure mode addressed: 42% of companies abandoned AI initiatives in 2025 (S&P Global). GenAI systems degrade silently: model drift, prompt degradation, cost spikes, quality regression. What happens: the Advance program activates. Monthly improvement cycles via Langfuse and CloudWatch Bridge. Prompt tuning, model optimization, drift detection, SLO management, cost optimization. The system gets better every month. This is Inferdat's Advance program.

### The Five Production Layers

These are not a post-deployment checklist. They are encoded into the modular infrastructure constructs and delivered in every BUILD engagement:

1. Observability: full visibility into every request, decision, and failure mode via Langfuse and CloudWatch Bridge
2. Security: guardrails, access controls, IAM patterns, and data protection baked into the architecture
3. Governance: audit trails, prompt versioning, compliance controls
4. Cost Control: per-request, per-user, per-month cost tracking with budget alerts
5. Reliability: drift detection, SLOs, cross-region inference fallbacks, consistent behavior at scale

### ProdWorks Construct Library

The internal engineering foundation that makes the 4x speed claim defensible. Modular CDK constructs organized in three layers:

Foundation Layer (shared across all solutions): Networking, Observability Platform (Langfuse on ECS + Aurora + CloudWatch Bridge), CI/CD pipelines, alerting, security baseline, demo shell.

Solution Layer (solution-specific building blocks): Agentic AI constructs, IDP constructs, Knowledge Assistant constructs, Analytics (ABI stack), SaaS-to-AI-Native constructs, AI-DLC constructs, Data Pipeline constructs, Predictive constructs.

Pattern Layer (pre-composed golden paths): Complete solution stacks composed from Foundation + Solution constructs. Standard deployments use patterns (fast). Custom deployments compose from individual constructs (flexible). Both produce production-grade output.

### Proprietary IP: CW-to-Langfuse Bridge

Inferdat's CloudWatch-to-Langfuse Bridge is proprietary integration that merges application-layer traceability with infrastructure monitoring in a single trace. This is what makes single-pane-of-glass operations possible and what makes the Advance program operationally viable. Competitors must build this themselves.

Learn more: https://inferdat.ai/prodworks/

## Solutions: 10 Production-Ready Data & AI Solutions on AWS

Inferdat offers 10 production-ready Data and AI solutions across three pillars. Each deploys in the customer's AWS account backed by ProdWorks™. All 10 are available as a free POC/V.

### Pillar 1: Operational AI & Enablement

Agentic AI & Multi-Agent Solutions: AI agents that pull data, make decisions, and complete multi-step processes across your systems. Full audit trail on every agent decision. Production-grade security with isolated execution. Automates workflows that currently require multiple people and handoffs.

Intelligent Document Processing: Automated extraction, classification, and processing of unstructured documents at scale. Turns manual document handling into an automated, auditable pipeline. Built with Bedrock Data Automation and Step Functions.

Autonomous Workflows: End-to-end workflow orchestration that connects systems, moves data, and executes processes without human intervention at each handoff point. Built with Step Functions, Lambda, and EventBridge.

Agentic AI Development Lifecycle: An orchestrated system of specialized AI agents that takes features from requirements through security review to production. Runs locally in Kiro IDE for everyday work, remotely on AWS AgentCore for governed builds. 50% credit savings vs unstructured AI coding, with security scanning at every phase. Built with Kiro IDE, Bedrock AgentCore, AWS Security Agent, and Cedar RBAC.

### Pillar 2: Customer-Facing GenAI & Monetization

SaaS to AI-Native: We add a conversational AI layer on top of your existing SaaS APIs. Your users talk to your product instead of clicking through it. No rewrite. Existing API routes become AI agent tools via AgentCore Gateway. Your auth model is replicated using Cedar policies. Deploys as standalone chat, embedded widget, or Slack/Teams bot. Built with Bedrock AgentCore Gateway, Cedar, and Bedrock Agents.

Knowledge Assistant: Plain language answers from internal docs, wikis, and drives with cited sources. Built-in guardrails for PII protection and hallucination prevention. Role-based access so users only see authorized sources. Built with Bedrock Knowledge Bases, OpenSearch Serverless, and RAGFlow.

AI-Powered Analytics (ABI): See ABI section above. Ranges from a managed BI deployment to a full white-label ABI™ platform for ISVs embedding analytics in their product.

### Pillar 3: Data Foundation for GenAI

Data Integration & Pipelines: Production-grade pipelines that move, transform, and deliver data reliably. Automated ingestion from any source into analytics-ready datasets with built-in data quality checks and full observability. Built with Glue, Step Functions, Kinesis, and EventBridge.

Data Trust & Governance: Automated data quality frameworks that continuously profile, validate, and monitor the data layer. Continuous anomaly detection and alerting. Quality dashboards your team can use. Every AI system is only as good as the data it runs on.

Predictive Analytics: Predict churn, forecast demand, and score leads without a data science team. Models trained on your historical data, not generic benchmarks. Business users get actionable predictions, not charts requiring interpretation. Built with SageMaker Canvas and SageMaker model endpoints.

73% of enterprise data goes completely unused for analytics (Forrester). Inferdat's solutions close this gap by making data accessible to non-technical users and automating insight delivery.

Learn more: https://inferdat.ai/solutions/

## Programs: Four Engagement Models

### POC/V: Prove Value First

Free proof of concept and value. All 10 solutions available. Results in days, not months. Customer owns everything built regardless of what they decide next. No SOW required to start.

The POC/V is Stage 2 (PROVE) of ProdWorks™. It runs in two parts: structured environment validation (data quality, API connectivity, compliance posture, AWS account readiness) followed by a working proof built on the customer's real data. Output includes a working proof-of-value and a validated Deployment Blueprint that Implement executes directly, eliminating re-discovery.

Duration: 1 to 5 days. Cost: free.

Learn more: https://inferdat.ai/programs/pocv/

### Assess: Know Before You Build

Four strategic assessment engagements and three technical health checks. Designed to uncover the highest-value automation opportunities, quantify ROI before budget is committed, evaluate GenAI security posture, and accelerate developer productivity. Every engagement produces a specific, actionable deliverable, not a generic consulting report.

The four assessments: GenAI Automation Assessment (2 to 4 weeks), GenAI ROI Assessment (2 to 3 weeks), GenAI Security Assessment (2 to 3 weeks), Developer Productivity Assessment (3 to 4 weeks).

The three health checks: GenAI Cost & Performance, GenAI Quality & Accuracy, Data Reliability & Efficiency.

$4.4T in annual productivity potential from GenAI across 63 use cases (McKinsey Global Institute). 74% of enterprises measuring GenAI ROI report positive returns (Wharton AI Adoption Report, 2025).

Duration: 2 to 4 weeks. Cost: fixed price, free scoping call.

Learn more: https://inferdat.ai/programs/assess/

### Implement: Build It Right

Production-grade builds on AWS. Two delivery models:

Project: defined scope, fixed price agreed upfront, full handoff. Five phases: Scope & Align (1 to 2 weeks), Architecture & Production Design (1 to 2 weeks), Build (agile sprints with bi-weekly demos), Validate & Harden (1 to 2 weeks), Launch & Transition (2 weeks including break-fix support). Typical total: 6 to 10 weeks.

Forward Deployed Pod: embedded senior team on a fixed monthly fee. No change orders ever. Senior Engineer, Technical Delivery Manager, and Inferdat Bench Access. Continuous cycle: Prioritize, Sprint, Deliver, Review. 3-month minimum commitment. Context compounds over time.

Only 5% of custom GenAI tools survive the pilot-to-production cliff without a production engineering foundation (MIT/Forbes). ProdWorks BUILD eliminates this gap by building production-grade from the first sprint.

Duration: 6 to 10 weeks for Projects, ongoing for Pod. Pricing: value-based for Projects, fixed monthly for Pod.

Learn more: https://inferdat.ai/programs/implement/

### Advance: GenAI-Native Managed Operations

Ongoing managed operations for AI systems in production. Not a generic MSP that bolted on a GenAI practice. GenAI operations require expertise several layers above infrastructure: evaluations, prompt management, per-agent SLOs, and token economics.

Powered by Inferdat Observe: a purpose-built GenAI operations platform with visibility into every agent decision, every tool call, and every token spent. Merges application-layer traceability (Langfuse) with infrastructure monitoring (CloudWatch Bridge) in a single trace. 30+ AWS services monitored across 6 categories.

Six operational capabilities: GenAI Quality Management, GenAI Cost Optimization, GenAI Security Operations, Operational Intelligence, Proactive Improvements, Drift Detection.

Two tiers:
- Essentials: quality monitoring, drift detection, 4-hour incident response, full infrastructure monitoring, monthly report and quarterly review
- Professional: everything in Essentials plus continuous prompt engineering, cost optimization, security guardrail tuning, closed-loop quality improvement, 1-hour response, dedicated success manager with bi-weekly cadence

42% of companies abandoned AI initiatives in 2025 (S&P Global). GenAI systems degrade silently after launch without specialized operations. Advance catches degradation before users do.

Duration: ongoing monthly subscription.

Learn more: https://inferdat.ai/programs/advance/

## Why Companies Choose Inferdat

Ex-Amazonians, not consultants: the team helped AWS customers succeed from inside Amazon. Same rigor, now from the outside.

Free POC/Vs: see it working on your real data, for free, before committing budget. Unusual in enterprise data and AI where most vendors require paid discovery before showing anything.

Value-based pricing: the customer knows the total cost before anything starts. No hourly billing, no surprise invoices.

ProdWorks™ on every solution: production-grade from day one, not a POC that got promoted. All five layers ship with every deployment.

4x faster to production: 6 to 10 weeks versus the 7 to 12 month industry average for GenAI deployments (Kore.ai).

One partner from build to operate: the team that builds is the same team that operates. No handoff gaps, no re-onboarding, no knowledge loss between Implement and Advance.

Customer's AWS account always: all solutions deploy in the customer's own AWS environment. Zero data egress. Critical for HIPAA, SOX, and FedRAMP-adjacent environments.

## Market Context

- $4.4T in annual productivity potential from GenAI across 63 use cases (McKinsey Global Institute)
- 88% of AI POCs fail to reach production (IDC/Lenovo)
- 50% of GenAI projects abandoned after POC due to poor data quality, inadequate risk controls, escalating costs, and unclear business value (Gartner, 2026)
- 42% of companies abandoned most AI initiatives in 2025, up from 17% in 2024 (S&P Global)
- Only 11% of companies have adopted GenAI at scale (McKinsey)
- Average enterprise GenAI deployment: 7 to 12 months from pilot to meaningful impact (Kore.ai)
- Embedded analytics market: $69.6B in 2024, projected $182.7B by 2033 (IMARC Group)
- 80% of B2B buyers now expect AI features in SaaS products by default
- 40% of enterprise applications will embed AI agents by 2026, up from 5% in 2025 (Gartner)
- 73% of enterprise data goes completely unused for analytics (Forrester)

## Frequently Asked Questions

Q: How much does Inferdat cost?
A: POC/Vs are free. Assess engagements are fixed-price. Implement projects are value-based: the customer knows the cost before anything starts. Advance is a monthly subscription. No hourly billing, no surprise invoices.

Q: Is the POC/V really free?
A: Yes. All 10 solutions are free as a POC/V. Custom POC/Vs are free if they fit the scope threshold. The customer owns everything built regardless of what they decide next.

Q: What is ABI and how is it different from BI tools like Tableau or QuickSight?
A: ABI is a decision intelligence platform, not a traditional BI tool. Tableau and QuickSight are internal work assistants for your employees. ABI delivers intelligence to both your employees and your customers, runs under your brand, requires no SQL skills, proactively surfaces insights before anyone asks, and charges a fixed monthly fee with no per-user pricing. It also deploys in your own AWS account so your data never leaves your environment.

Q: Can ABI be white-labeled as the customer's own product?
A: Yes. ABI deploys in the customer's AWS account, runs under their brand, and end users never see Inferdat. Fixed monthly pricing, not per-seat.

Q: What is ProdWorks and is it just a framework?
A: ProdWorks is no longer positioned as a framework. It is a 4-stage accelerated deployment process (SHOW, PROVE, BUILD, OPERATE) that eliminates the four root causes of GenAI project failure and delivers production-grade systems in 6 to 10 weeks, 4x faster than the industry average. The five technical layers (Observability, Security, Governance, Cost Control, Reliability) are what gets built inside that process.

Q: Where does the data go?
A: Nowhere. All solutions deploy in the customer's own AWS account. Data never leaves their environment, not to Inferdat, not to any third party.

Q: What does Advance manage?
A: Everything required to keep a GenAI system healthy in production: quality monitoring, cost optimization, drift detection, prompt engineering, model benchmarking, security operations, and SLO management. Powered by Inferdat Observe, which provides single-pane-of-glass visibility across both the application layer and infrastructure layer.

Q: How long does ABI take to deploy?
A: Minutes from AWS Marketplace via a single CloudFormation template. Training on the customer's schema takes minutes to hours. Most customers run real queries the same day.

Q: What is the difference between POC/V and Assess?
A: POC/V is the right starting point when you already know which use case to pursue and want to validate it works on your real data before committing to a full build. Assess is the right starting point when the question is "what should we build first" or "is our existing system healthy." Assess produces the roadmap. POC/V validates the first item on that roadmap.

## Contact

- Website: https://inferdat.ai/
- Contact and inquiries: https://inferdat.ai/contact/
- Blog (Pulse): https://inferdat.ai/pulse/
- Success Stories: https://inferdat.ai/success-stories/
- ABI product page: https://inferdat.ai/abi/
- ProdWorks: https://inferdat.ai/prodworks/
- Solutions: https://inferdat.ai/solutions/
- POC/V program: https://inferdat.ai/programs/pocv/
- Assess program: https://inferdat.ai/programs/assess/
- Implement program: https://inferdat.ai/programs/implement/
- Advance program: https://inferdat.ai/programs/advance/

## Client Results & Testimonials

### TalkNotez — Voice-to-Action AI on Amazon Bedrock

TalkNotez is a consumer voice productivity app that needed sub-three-second processing within a $2.99/month subscription price point. Inferdat deployed a multi-model inference pipeline on Amazon Bedrock handling real-time transcription, entity extraction, and action item generation.

Results: Sub-3-second end-to-end latency. Inference cost under $0.003 per request. Bulk backlog processing for new subscriber onboarding. Pro conversion uplift post-launch.

Duration: 6 weeks. AWS Services: Amazon Bedrock, AWS Lambda, Amazon S3, Amazon CloudWatch.

> "We needed cloud AI that worked within a $2.99 subscription. Inferdat got us sub-three-second processing on Bedrock, handled the bulk backlog flow for new subscribers, and made the economics work. Pro conversions are up since."
> — Paul Appia, CTO & Co-Founder, TalkNotez

### Little Linked Librarian — Multi-Agent Insights for 150,000 Libraries

Little Linked Librarian connects 150,000+ Free Little Library boxes worldwide. The team had no mechanism to proactively identify underserved locations, demand gaps, or declining engagement across the network. Inferdat deployed a multi-agent proactive insights architecture on AWS using Amazon Bedrock for agent orchestration.

Results: 25+ high-frequency demand patterns identified in first 30 days. 8 underperforming location clusters surfaced within 60 days. 80% reduction in manual network analysis time. Daily ranked insight feed replaced reactive investigation.

Duration: 7 weeks. AWS Services: Amazon Bedrock, Amazon Bedrock Guardrails, Amazon EventBridge, AWS Lambda, Amazon Aurora PostgreSQL, Amazon S3, Amazon CloudWatch, Amazon SNS. Model: Claude Sonnet on Amazon Bedrock.

> "We were flying blind, honestly. Books were getting scanned, nodes were active, but we had no clue which communities actually needed help or where we were losing momentum until it was already gone. Inferdat built us something that watches the whole network and just tells us what to do. Now I open a list every morning of what needs attention. For a small team trying to connect 150,000 library boxes, that's huge."
> — Zack, Founder & CTO, Little Linked Librarian

### 10xLab — Enterprise RAG Deployment on AWS Bedrock

10xLab had a successful AI product but lost enterprise deals where prospects required AWS-native deployment without managing GPU infrastructure. Inferdat built a parallel deployment path using Amazon Bedrock for inference without forking the existing codebase.

Results: New enterprise segment opened. Same product serves both self-hosted GPU and AWS Bedrock paths. No codebase fork required. Deals previously lost to infrastructure objections now closeable.

Duration: 8 weeks. AWS Services: Amazon Bedrock, Amazon S3, AWS Lambda, Amazon OpenSearch Serverless. GenAI Pattern: RAG.

> "We kept losing deals where the prospect loved the product but didn't want to manage GPU infrastructure. Inferdat built us an AWS path that uses Bedrock for inference without forking the codebase. Same product, two deployment options. That opened up a whole segment we couldn't reach before."
> — James Lai, CEO, 10xLab