Common questions, straight answers.

Generative AI uses large language models (LLMs) to create content, analyze data, and automate complex tasks. For businesses, it can automate document processing, power intelligent customer service chatbots, enable natural language search across internal knowledge bases, generate reports and summaries, and accelerate software development. Our clients typically see 60-80% efficiency gains in targeted workflows.

RAG is a technique that combines a large language model with your company's proprietary data. Instead of relying solely on the model's training data, RAG retrieves relevant documents from your knowledge base and uses them to generate accurate, grounded responses. This means the AI answers are specific to your business, reducing hallucinations and ensuring responses are based on your actual data, policies, and documentation.

GenAI implementation costs depend on scope and complexity. A focused proof-of-concept typically costs $30,000-$60,000 over 3-4 weeks. Production implementations range from $80,000-$200,000+ depending on integration requirements, data volume, and security needs. Amazon Bedrock's pay-per-use pricing means ongoing costs are proportional to actual usage, typically $500-$5,000/month for most enterprise workloads.

Yes, when using Amazon Bedrock on AWS, your data stays within your AWS account and is encrypted at rest and in transit. Your data is never used to train the underlying foundation models. We implement defense-in-depth security including VPC isolation, IAM least-privilege access, AWS KMS encryption, CloudTrail audit logging, and compliance with SOC 2, HIPAA, and other regulatory frameworks as needed.

Through Amazon Bedrock, we work with leading foundation models including Anthropic Claude, Amazon Titan, Meta Llama, Mistral, and Cohere. We help clients select the best model for their specific use case based on accuracy, latency, cost, and compliance requirements. We also offer custom model fine-tuning on your proprietary data for industry-specific accuracy improvements.

A proof-of-concept can be delivered in 2-4 weeks. A production-ready GenAI solution typically takes 8-16 weeks depending on complexity, integration requirements, and compliance needs. We follow an agile approach with weekly demos so you see progress throughout the engagement. Many clients start with a focused use case and expand to additional workflows after seeing initial results.

It's a fixed-scope discovery engagement run before any build work. Over roughly 2 to 3 weeks we evaluate your goals, data, and infrastructure, then hand you a prioritized use-case roadmap, a reference architecture on AWS, an ROI and cost model, and a security and compliance review.

Most assessments run 2 to 3 weeks depending on the number of use cases and the state of your data. You leave with a concrete plan, not a vague strategy deck.

It's a fixed fee, scoped on a short discovery call, so you know the full cost before you commit. Pricing depends on the number of use cases and the complexity of your data and compliance requirements.

No. The deliverables are yours to keep and build with any team. That said, many clients continue with us to implement what the assessment recommends, since we've already mapped the architecture and the risks.

Yes. We've delivered production AI for regulated healthcare enterprises including LabCorp and VRC, with HIPAA constraints such as keeping PHI out of logs. Security and compliance are embedded in every phase, not bolted on at the end.

Very little. We work from your existing business goals, sample data, and access to the right stakeholders. Part of the assessment is telling you exactly where your data and infrastructure are ready and where the gaps are.

The biggest driver of self-hosted cost is throughput — how many pages each GPU instance processes per month. Because that varies by document complexity and pipeline tuning, we show a conservative-to-optimistic band around our default estimate rather than a single number. You can try the calculator here.

Most financial-services implementations take 8-12 weeks from kickoff to production, covering discovery, architecture, build, testing, and deployment. We can usually put a proof-of-concept in front of your underwriting or operations team within 2-3 weeks.

Typically 92-99% depending on document quality. Handwritten loan forms reach about 92% with Amazon Bedrock, and low-confidence fields route to a human reviewer, so critical decisions are never left to a guess.

Yes. Solutions run in your own AWS account, which maintains SOC 1/2/3 and PCI DSS compliance. We add audit trails, encryption at rest and in transit, and role-based access so the pipeline fits inside your existing controls.

Clients typically see a 40-65% reduction in processing time and around 40% cost savings. Bank of Montreal reached $2.1M in annual operational savings, cutting loan turnaround from 5-7 days to 1.5-2 days.

We use AWS Textract to extract shipping-manifest data from Excel files, then integrate with third-party logistics APIs for real-time tracking across trucks and ships. It removes manual data entry and gives customers accurate delivery estimates. We built this for Porsche Digital.

Yes. We pair AWS Textract for PDF ingestion with Amazon Translate for multi-language conversion of safety manuals, compliance documents, and technical specs. Volkswagen uses this to distribute safety manuals from German into every market's language.

Textract extracts data from purchase orders, invoices, and receiving documents, then the system matches and reconciles them automatically. Human-in-the-loop validation (A2I) handles exceptions, so accuracy stays high while manual effort drops. Samtec runs this today.

We work with automotive manufacturers (Porsche, Volkswagen), electronics companies (Samtec), and other manufacturers with heavy logistics and documentation needs. The same building blocks adapt across supply-chain and compliance workflows.