Salt Technologies AI AI
Starting at $8,000

AI Proof of Concept Sprint

Know whether your AI idea will work before you invest $50,000 in building it. Working prototype. Your real data. 2-4 weeks.

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Before you commit $50,000 or more to a full AI build, you need proof it will work with your data. The AI Proof of Concept Sprint delivers a working AI prototype in 2-4 weeks, built with your actual data, so you can validate technical feasibility, measure real-world accuracy and latency, benchmark cost-per-query economics, and demonstrate value to stakeholders with a live demo. If the prototype meets your success criteria, you have a clear, costed path to production. If it does not, you saved a significant investment by discovering it early, and you still own the code and architecture docs.

Why Most AI Projects Fail (and How to Avoid It)

The biggest risk in AI is not the technology. It is investing six figures before knowing whether the technology works with your specific data.

87%

Of AI projects never make it to production

Most fail not because the technology is wrong, but because teams skip validation and discover data problems after investing $50,000 or more.

$50K+

Wasted on AI that looked great in the demo

Vendor demos use clean data. Your data has edge cases, missing fields, and domain-specific quirks. The only way to know is to test with YOUR data.

2-4 wk

Is all it takes to eliminate the guesswork

A proof of concept sprint gives you a working prototype, real benchmarks, and a clear Go/No-Go recommendation before you commit to a full build.

14+

Years of Experience

800+

Projects Delivered

100+

Engineers

4.9★

Clutch Rating

What You Get from the AI Proof of Concept Sprint

Working AI prototype deployed to a staging environment and testable by your team
Technical architecture document: how the PoC scales to production with infrastructure recommendations
Performance benchmarks: accuracy, latency, throughput, and cost-per-query metrics
Go/No-Go recommendation with detailed production cost estimate and timeline
Source code and full documentation (yours to keep regardless of outcome)
Model comparison report (when multiple AI approaches are evaluated)

Everything we build is yours to keep: source code, documentation, models, and benchmark data. No lock-in. No licensing fees. Use it however you want, even if you choose to build production with a different partner.

How the AI Proof of Concept Sprint Works

1

Scope Definition

2-3 days

Define the use case, success criteria, data requirements, and benchmarks the PoC must hit to be considered successful.

2

Data Preparation

2-3 days

Clean, format, and prepare your data for the AI model. Assess data quality and identify any gaps that could affect results.

3

Build Sprint

1-2 weeks

Develop the AI prototype, iterate on accuracy and performance, test against success criteria, and optimize for your specific use case.

4

Demo and Handoff

1 day

Present the working PoC with benchmark results, walk through the architecture document, share all source code, and discuss the production path forward.

AI Proof of Concept: Pricing and Timeline

Timeline

2-4 weeks

Starting At

$8,000

What Affects Pricing

  • Complexity of the AI model (simple classification vs. RAG pipeline vs. multi-agent workflow)
  • Data preparation effort required (clean CSV vs. unstructured PDFs vs. multi-source data)
  • Number of integration points with existing systems
  • Whether custom model training or fine-tuning is needed
  • Number of AI models or approaches to evaluate side by side

Get a fixed quote before any work begins

Tell us about your AI use case. We will scope it and give you an exact price and timeline. No hourly billing. No surprise invoices.

Get Your Fixed Quote
Free 30-min discovery call Fixed price, no surprises You own everything we build

Have a specific AI use case in mind? Let's scope your proof of concept.

Scope Your PoC

AI Proof of Concept Examples

Real scenarios. Real benchmarks. Real decisions informed by PoC results.

RAG Knowledge Base PoC

2 weeks

The Challenge

A SaaS company with 5,000+ support articles wanted to test whether AI could answer customer questions more accurately than their keyword search.

What We Built

Built a RAG prototype indexing 500 representative articles, tested with 200 real customer queries, measured answer accuracy and retrieval precision.

Result

92% answer accuracy with citations. 3x faster resolution than manual search.

Next Step

Client approved $15,000 production build.

Document Extraction PoC

3 weeks

The Challenge

An insurance company processing 10,000 claims per month wanted to automate data extraction from claim forms and supporting documents.

What We Built

Trained extraction model on 300 sample claim documents, tested field-level accuracy across 15 data points per document.

Result

94% field-level extraction accuracy. Projected 70% reduction in manual processing time.

Next Step

Client moved to full AI Integration Sprint.

AI Agent Workflow PoC

3 weeks

The Challenge

A fintech startup wanted to validate an AI agent that could research companies, pull financial data from APIs, and generate investment summaries.

What We Built

Built multi-tool agent with 4 API integrations, tested against 50 real research requests, measured accuracy and processing time.

Result

85% of tasks completed without human intervention. Processing time dropped from 45 to 3 minutes.

Next Step

Client invested in full Custom AI Agent build.

What If the PoC Shows AI Will Not Work?

Then you just saved $50,000 or more. A "No-Go" is not a failure. It is the most valuable outcome you can get from an $8,000 investment.

Even if the answer is "No," you still walk away with:

A detailed analysis of exactly why AI did not meet your benchmarks
Specific limiting factors identified: data quality, model capabilities, or use case complexity
Alternative approaches recommended if they exist
All source code, architecture docs, and benchmark data (yours to keep)
The confidence to redirect your AI budget to an approach that will actually work

We have told clients "No-Go" when the data showed it. We would rather save you $50K than take a fee for a production build we know will underperform. That honesty is why clients trust us with their next project.

Types of AI Proofs of Concept We Build

Not sure which type of AI prototype fits your use case? Here are the most common PoCs we build.

AI Chatbot and Copilot PoC

Test whether a chatbot trained on your data can achieve the accuracy, tone, and hallucination rates you need before building the full system.

RAG Document Q&A PoC

Prove that AI can answer questions from your documents with citations. Test retrieval precision across PDFs, DOCX, HTML, and Confluence.

AI Classification and Extraction PoC

Validate AI extraction accuracy on your documents: invoices, contracts, support tickets, or medical records. Measure speed and error rates.

Predictive Analytics PoC

Test whether your historical data supports accurate predictions for churn, demand, lead scoring, or anomaly detection.

AI Agent Workflow PoC

Validate that an autonomous AI agent can execute multi-step workflows: calling APIs, making decisions, and taking actions with guardrails.

AI Search and Recommendations PoC

Prove that semantic search or AI recommendations outperform your current keyword search with real queries from your users.

Is the AI Proof of Concept Sprint Right for You?

This is for you if...

  • You have a specific AI use case identified (from an audit or your own research)
  • You want to see AI working with your data before committing $50K+ to a full build
  • You need to demonstrate AI value to leadership, investors, or board members with a real demo
  • You want to test technical feasibility, accuracy, and latency with your actual data
  • You are evaluating multiple AI approaches and need benchmarks to decide
  • You want to understand per-query costs and infrastructure requirements before scaling

This is NOT for you if...

Why Choose Salt Technologies AI for Your Proof of Concept

We are not a consulting firm that delivers slide decks. We are the engineers who will build your prototype, and the same team that can take it to production.

Real Prototypes, Not Slide Decks

We deliver a working, testable AI system built with your data, not a presentation about what could be built. You interact with the prototype, test edge cases, and see real performance numbers.

Honest Go/No-Go Recommendations

If the data shows AI will not meet your benchmarks, we tell you. Saving you $50K+ on a doomed project is a win, not a failure. We document exactly why it did not work and recommend alternatives.

Production-Ready Architecture Docs

Every PoC comes with a technical architecture document showing exactly how to scale it to production, including infrastructure requirements, cost projections at scale, and implementation timeline.

Code and IP Are Yours to Keep

All source code, documentation, models, and benchmark data created during the sprint belong to you. No lock-in, no licensing fees, regardless of whether you proceed with a full build.

Same Team Can Build Production

Unlike pure consulting firms that hand off a report, our AI engineers can take the validated PoC to production in a follow-up engagement. No knowledge transfer gap, no ramp-up time, no re-learning your data.

Benchmarks Against Your Success Criteria

We do not declare success based on vibes. Every PoC is tested against the specific accuracy, latency, and cost targets you define before we start building. Results are documented in a formal performance report.

AI Proof of Concept vs. Full Production Build

Not sure which you need? Most companies start with a PoC and move to production after validating feasibility.

Goal

Proof of Concept

Validate feasibility with real data

Full Build

Ship production-ready software to users

Timeline

Proof of Concept

2-4 weeks

Full Build

2-6 months

Investment

Proof of Concept

$8,000-$18,000

Full Build

$50,000-$200,000+

Output

Proof of Concept

Working prototype + benchmarks + Go/No-Go

Full Build

Production system with monitoring and scale

Data Needs

Proof of Concept

100-1,000 representative samples

Full Build

Full production dataset

Risk

Proof of Concept

Low: small investment, clear outcome

Full Build

High without prior validation

Users

Proof of Concept

Internal team, stakeholders

Full Build

End users, customers

Infrastructure

Proof of Concept

Staging environment

Full Build

Production cloud with HA, monitoring, backups

Our recommendation

Start with a proof of concept when you have not validated AI feasibility with your specific data. The $8,000 PoC investment prevents $50,000+ in wasted development. Already validated? Skip to an AI Integration Sprint or AI Chatbot Build.

AI Proof of Concept Technology Stack

We select the right AI models, frameworks, and infrastructure for your specific use case. No one-size-fits-all.

OpenAI GPT-4o Anthropic Claude Google Gemini LangChain LlamaIndex Pinecone Weaviate pgvector Python FastAPI Docker LangSmith

AI Proof of Concept: Frequently Asked Questions

How much does an AI proof of concept cost?
The AI Proof of Concept Sprint starts at $8,000 for a standard engagement. Final pricing depends on the complexity of the AI model, data preparation effort, number of integrations, and whether multiple approaches need to be evaluated. Multi-agent PoCs typically range from $12,000 to $18,000. You receive a fixed quote after the scope definition phase, before any build work begins.
What is an AI proof of concept and why do I need one?
An AI proof of concept (PoC) is a working prototype that validates whether AI can solve your specific business problem using your real data. You need one because AI performance varies dramatically based on data quality, use case complexity, and model selection. A PoC costs $8,000 to $18,000 and takes 2-4 weeks. A failed full build costs $50,000 or more and takes months. The PoC eliminates that risk.
How long does it take to build an AI proof of concept?
Typically 2-4 weeks from kickoff to final handoff. Simple PoCs (single model, clean data, one use case) can be completed in 2 weeks. More complex PoCs involving multi-agent systems, multiple data sources, or side-by-side model comparisons take 3-4 weeks. Timeline is fixed during the scope definition phase.
How is the AI PoC Sprint different from the AI Readiness Audit?
The AI Readiness Audit ($3,000, 1-2 weeks) tells you WHERE to apply AI by assessing your business, data, and systems. The PoC Sprint ($8,000, 2-4 weeks) BUILDS a working prototype to prove a specific AI idea works with your data. If you already know what you want to build, skip the Audit and go straight to the PoC Sprint. If you are not sure where AI fits, start with the Audit.
What is the difference between an AI proof of concept and an AI MVP?
A proof of concept validates technical feasibility: can AI achieve the required accuracy, latency, and cost targets with your data? An MVP (minimum viable product) is a stripped-down production system that real users interact with. The PoC comes first to prove the idea works. If it passes, we build the MVP or full production system in a follow-up engagement. Do not skip the PoC and jump to MVP; that is how companies waste $50K+ on AI projects that were never technically feasible.
Do I own the code and intellectual property?
Yes. All source code, documentation, trained models, benchmark data, and architecture documents created during the sprint are yours to keep. There are no licensing fees, no lock-in, and no restrictions on how you use the deliverables, regardless of whether you proceed with a full build through us or another vendor.
What if the PoC shows AI will not work for my use case?
That is actually a win. You saved $50,000 or more by discovering it early instead of halfway through a production build. We provide a detailed analysis of why the PoC did not meet your benchmarks, what the specific limiting factors were (data quality, model capabilities, use case complexity), and recommend alternative approaches if they exist.
Can the PoC become the production system?
PoCs are built for validation speed, not production durability. However, the technical architecture document we deliver outlines exactly how to take the validated PoC to production, including infrastructure requirements, scaling considerations, security hardening, and cost projections. We can execute that production build in a follow-up AI Integration Sprint or AI Chatbot Development engagement.
What data do you need from us to build the AI prototype?
It depends on the use case. For document Q&A or RAG, we need sample documents (PDFs, DOCX, or HTML). For classification, we need labeled examples. For chatbots, we need your knowledge base content. For predictive analytics, we need historical data. We define exact data requirements during the scope definition phase and typically need 100-1,000 representative samples to build a meaningful prototype.
How do you measure whether the PoC is successful?
We define measurable success criteria with you before building: accuracy targets (e.g., 90%+ correct answers), latency thresholds (e.g., under 3 seconds per query), cost-per-query limits (e.g., under $0.05 per interaction), and any domain-specific metrics. The PoC is tested against these benchmarks with your real data, and results are documented in the formal performance report.
Can you build a PoC for a multi-agent AI system?
Yes. Multi-agent PoCs are more complex and typically fall in the $12,000 to $18,000 range with a 3-4 week timeline. We scope these carefully to test the most critical agent interactions first, then expand. The PoC validates agent orchestration, tool integration, decision-making accuracy, and error handling before committing to a full multi-agent production build.
What industries do you build AI proofs of concept for?
We have built AI PoCs for SaaS companies, healthcare organizations, fintech firms, e-commerce businesses, legal services, insurance companies, and manufacturing firms. Industry-specific PoCs account for compliance requirements (HIPAA, SOC2, PCI-DSS), domain-specific data formats, and regulatory constraints. Our AI engineers have experience with industry-specific data challenges across all of these verticals.
Can you build an AI proof of concept for a startup with limited data?
Yes. Startups often have smaller datasets, but that does not disqualify a PoC. We use techniques like few-shot learning, synthetic data augmentation, transfer learning, and pre-trained models to work with limited data. The PoC also identifies the minimum data requirements for production performance, so you know exactly what to collect as you scale.
How do you handle confidential or sensitive data during the PoC sprint?
We sign NDAs before every engagement. Data access is limited to the engineers assigned to your sprint. We can work within your VPN, use your cloud infrastructure, or build in air-gapped environments for highly sensitive data. For healthcare and financial data, we follow HIPAA and SOC2 protocols respectively. We never retain your data after the engagement ends.
What happens after the PoC if we want to go to production?
If the PoC validates your use case, we provide three paths forward: (1) AI Integration Sprint to embed the validated AI into your existing product, (2) AI Chatbot Development or RAG Knowledge Base for specific production builds, or (3) AI Managed Pod for ongoing AI development. The same engineers who built your PoC can execute the production build, eliminating knowledge transfer overhead.

Getting Started with Your AI Proof of Concept

No lengthy procurement process. No upfront commitment. Three steps to a working prototype.

1

Book a Free Call

30-minute discovery call. Describe your AI use case, your data, and what you need the prototype to prove. No sales pitch.

2

Get a Fixed Quote

We scope your proof of concept with exact price, timeline, and success criteria. You approve before any work begins.

3

We Build Your PoC

In 2-4 weeks: working prototype, benchmarks, architecture docs, and a clear Go/No-Go recommendation.

Start Your AI Proof of Concept

Not sure which AI use case to validate first?

Start with the $3,000 AI Readiness Audit to identify your best opportunity

After the PoC: Your Path to Production

A validated PoC is the foundation, not the finish line. The same team that built your prototype can take it to production.

AI Chatbot or RAG Build

Deploy a production chatbot or knowledge base with admin dashboard and integrations.

From $12,000

AI Integration Sprint

Embed the validated AI capability directly into your existing product. No rewrite.

From $15,000

AI Managed Pod

Continue with a dedicated team that already knows your data, your domain, and your code.

From $12,000/mo

Ready to validate your AI idea?

Start with a 30-minute call. No commitment. No pitch. Just a conversation about what your AI proof of concept could look like.

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