US-based · 12+ years enterprise AI · Performance-based engagements
Generative AI for
Operations You Can't Get Wrong
US-based team building LLM applications, AI agents, and custom AI models for businesses in healthcare, manufacturing, and financial services from SMB to mid-market to enterprise. Microsoft, AWS, and Google Cloud certified.
Trusted by
Philips
Nationwide
Penn Medicine
Forbes
Ohio State
Serving Fortune 500s and Healthcare Leaders
Why Sunflower Lab
Why enterprises choose us for Gen AI
We're not a typical AI consultancy. We build production systems and tie our engagement to outcomes — not just billable hours.
A reliable track record
With over 32+ industries and 200+ projects, we have served our clients only the best. Across our Gen AI engagements, clients have documented an average of 40–60% reduction in manual processing time within 90 days of go-live. That track record is what earns us the next engagement, not a contract clause.
Client-FirstAI that's auditable, controlled, and compliant by design
Every solution we deploy is built against a defined AI governance framework: role-based access controls, data classification boundaries, human-in-the-loop checkpoints for high-stakes decisions, and full audit trails for model inputs and outputs. We document what the model can and cannot do, and we enforce those guardrails in codes.
SOC 2 • HIPAA • GDPRWe run AI in our own operations
We use AI agents internally for proposal generation, RPA for our own back-office, and LLM applications for client research. We implement what we recommend, so our advice is grounded in what actually works in production, not what looks good in a vendor deck.
Practitioner-ledWhat We Build
Generative AI development services
End-to-end LLM engineering — from consulting and use-case scoping to production deployment on your preferred cloud platform.
Generative AI development
Production AI applications that read, sort, summarize, and respond to language work at business speed, processing invoices, customer emails, case notes, contracts, or support tickets. Built with retrieval augmented generation (where the AI looks up your actual company data before answering, so it doesn't make things up) and human-review checkpoints where the stakes are high. Typical day-one outcome: 40 to 70 percent reduction in time-per-task, with a full audit trail of every AI decision.
Model training & customization
We take a general AI model and train it on your business, your terminology, your customers, your decision rules — so it stops sounding like generic ChatGPT and starts sounding like your team. We work with Microsoft Azure, Amazon, and Google's AI platforms, plus open-source models like Llama and Mistral when those fit better. Your data stays in your own cloud account. Nothing leaves your control.
Custom generative AI models
For situations where off-the-shelf AI isn't a fit, usually because your data is too specialized, your accuracy bar is too high, or you operate in a tightly regulated industry- we build AI models from the ground up. Higher upfront investment, but it pays back when you're processing very high volumes (millions of documents or queries a month) or when "close enough" isn't good enough.
Generative AI consulting
A two- to four-week structured engagement: we look at where AI would actually make a difference in your business, check whether your data is ready for it, compare buying off-the-shelf versus building custom, and write you a roadmap with realistic costs and risks called out. You walk away with a 20- to 30-page decision document your CFO can fund or that gives you confidence to wait.
Azure OpenAI applications
Microsoft's enterprise version of ChatGPT and GPT-4, running inside your own Microsoft tenant. Uses single sign-on with your existing Microsoft accounts, fits the Microsoft 365 compliance setup you already have, and keeps all your data inside Microsoft's compliance boundary. Best fit for businesses heavily invested in Microsoft, and for healthcare or finance teams where compliance paperwork (BAAs and similar) is non-negotiable.
Amazon Bedrock Applications
Full-stack development on AWS Bedrock for organizations standardized on AWS. Foundation model selection across Anthropic Claude, Meta Llama, Amazon Titan, Cohere, and Mistral. Includes Bedrock Knowledge Bases (managed RAG), Bedrock Agents (multi-step task execution), Guardrails configuration, and deployment via AWS Lambda, ECS, or SageMaker. PrivateLink and KMS encryption by default.
Technology Comparison
Generative AI vs. RPA vs. traditional software
Know which technology is right for your use case before you invest. Here's how they differ for real enterprise scenarios.
| Criteria | Generative AI LLMs · Agents · NLP | RPA Bots · Rules · Workflows | Traditional Software Apps · Logic · Custom Dev |
|---|---|---|---|
| Best for | Unstructured data, language tasks, document understanding | Structured, rule-based, repetitive processes | Complex logic, custom enterprise apps |
| Adapts to variation | Yes Handles edge cases & ambiguous inputs |
✕ No Breaks on format or process changes |
✕ No Requires code changes for new logic |
| Time to deploy | 8–16 weeks | 4–12 weeks | 12–24 weeks |
| Needs training data | Yes — Fine-tuning or RAG | ✕ No — Process mapping only | ✕ No — Explicitly programmed |
| Best fit for | Document processing, query automation, conversational interfaces | Invoice processing, HR workflows, ERP data entry | Custom enterprise platforms, complex business logic |
| How it works | LLMs interpret language, extract meaning, generate responses, or take actions- trained on your data via RAG or fine-tuning to ground outputs in your context | Bots follow scripted step-by-step rules to move, enter, or transform data across systems- no reasoning, pure execution | Developers write explicit logic: if X then Y; every behavior defined in code; system does exactly what it's programmed to do |
| Integration | Connects via APIs to existing apps, databases, and document sources; layers on top of current systems without replacing them | Integrates at the UI or API layer of existing systems; mimics user actions or calls APIs to move data between tools | Deep, native integration built to spec; requires defined APIs or data contracts upfront |
| Compliance fit | Strong when paired with audit trails, prompt logging, output guardrails, and human-in-the-loop review | Strong for deterministic compliance tasks; weaker for judgment-based ones | Strong but expensive to extend |
Industry Use Cases
Where Gen AI delivers ROI
Specific workflows — by industry — where our clients see the fastest returns.
Manufacturing operations AI
Manufacturing operations leaders use generative AI to compress unstructured maintenance, work order, and field report data into structured action items. Typical applications:
- Summarizing handwritten or transcribed field reports into CMMS entries
- Extracting failure modes from technician notes for root-cause clustering
- Routing supplier compliance documents through automated classification
Production-ready in 10-14 weeks for a single workflow.
Manual report processing time
8h → 45m
Daily report processing
92%
Work order accuracy, vs. 61% manual
10×
Faster supplier doc throughput
0
Rework loops
Healthcare AI workflows
Healthcare automation under HIPAA controls:
- Prior-authorization request preparation from EMR data
- Case scheduling and intake triage
- Claims denial appeal letter drafting
- Clinical document summarization for utilization review
Our healthcare deployments include audit trails on every AI-generated output, prompt-and-response logging, and human-in-the-loop review for any action that touches a clinical record.
Chart review time per patient
22m → 5m
Chart review per patient
3×
Faster prior-auth decisions
65%
Patient queries automated
+
Full audit trail retained
Supply chain intelligence
Supply chain teams apply generative AI to:
- Supplier risk assessment from news and SEC filings
- Contract clause extraction across thousands of supplier agreements
- Demand-signal summarization from unstructured customer correspondence
- Customs documentation classification
Typical first deployment: contract clause extraction, where SFL has reduced legal review time by 60-80 percent on standardized supplier paperwork.
RFQ processing cycle time
4d → 4h
RFQ processing cycle
↓90%
Supplier response time
Financial services automation
Financial services applications focused on regulated, auditable use cases:
- KYC document understanding
- Suspicious activity report (SAR) drafting from transaction narratives
- RFP and pitchbook generation
- Earnings-call transcript summarization for investor relations teams
Every deployment includes governance documentation aligned to FINRA and OCC supervisory expectations for AI in financial services.
Compliance document review time
12h → 90m
Compliance review per document
95%+
Email routing accuracy, vs. 73% manual
Education platforms
Higher-education and EdTech generative AI use cases:
- Institutional knowledge base assistants for faculty and staff, admissions essay first-pass review (always paired with human reviewers)
- Syllabus and curriculum content generation
- Research librarian assistant agents that retrieve and summarize across institutional repositories
We have delivered AI projects with Ohio State University
Instructor feedback time per student
25m → <2m
Feedback time per student
+41%
Completion with adaptive paths
Case Studies
Enterprise AI Results in Action
Read how our customized Gen AI solutions drive efficiency, reduce manual workloads, and deliver measurable ROI.
Case Study — SDC
50% faster proposal generation with Gen AI-powered site research
SDC's project managers were buried. Across 72+ municipalities, every proposal required hours of manual site research, zoning lookups, parcel data, NPDES triggers, floodplain checks before a single word of proposal copy could be written. We built an Gen AI-powered proposal platform on AWS Bedrock and LangGraph that pulls site-specific data automatically from county GIS, PA DEP, FEMA, and PennDOT systems, walks PMs through a section-by-section review workflow, and generates a client-ready DOCX in minutes.
50%
Faster proposal generation
72+
Municipalities covered
Minutes
To generate client-ready DOCX
Case Study — Holiday Décor Brand
Days to minutes design prototyping with custom creative AI agent
We built a multi-modal creative assistant and custom AI agent for a seasonal holiday decor leader. It accepts natural language prompt adjustments, respects complex brand guidelines, and creates high-fidelity design prototypes, slashing designer drafting timelines from days to minutes.
Days → Mins
Design drafting time
100%
Brand guideline compliance
4 Weeks
To first working prototype
Case Study — ZinniaX
80% Reduction in paperwork & 5x faster clinical scheduling
ZinniaX — a clinical workflow platform for IONM/EEG providers — came to us with a manual scheduling problem. Coordinators were spending hours per day on data entry and phone tag. We built a custom generative AI scheduling assistant and implemented OCR document capture. This HIPAA-compliant solution automates appointment creation, eliminates manual data entry, and reduces clinical scheduling workloads, allowing medical staff to focus on patient care.
80%
Paperwork & data entry reduction
5x
Faster clinical scheduling
0
Manual data entry steps
Common Questions
FAQ
Generative AI development is the process of building applications powered by large language
models (LLMs) that can generate text, analyze documents, answer questions, and automate workflows. At
Sunflower Lab, we build enterprise generative AI applications on platforms like Azure OpenAI, Amazon
Bedrock, and Google Vertex AI — integrated into your existing business systems.
Engagements typically range from $10,000 for focused use-case applications to $200,000+ for
enterprise-scale LLM platforms with multiple integrations. We operate on a performance-based model — meaning
we tie our engagement to outcomes, not just hours billed. A free 30-minute discovery call is the best way to
get a scoped estimate.
Most enterprise implementations take 8–20 weeks from discovery to production. A focused use
case (such as automating customer query routing or building an internal document Q&A tool) can go live in
8–10 weeks. Multi-system integrations or custom model fine-tuning projects typically run 14–20 weeks.
Traditional software follows explicit rules programmed by developers. Generative AI learns
from data and generates outputs — text, analysis, recommendations — without every rule being pre-coded. This
makes it far better suited to tasks like document understanding, intelligent routing, and natural language
workflows. Gen AI applications adapt to changing inputs; traditional apps require code changes.
You're likely a strong candidate if you have high-volume document or data processing tasks,
repetitive knowledge-retrieval workflows, or customer-facing queries following predictable patterns.
Manufacturing, healthcare, and financial services companies with structured data and clear operational pain
points typically see the fastest ROI. If you're unsure, our use-case audit is the best starting point.
Yes, and this is where enterprise generative AI is moving fastest. Multimodal systems
accept text, image, audio, and video as input and output coherent intelligence across all of them- useful
for quality inspection, training simulations, and customer-facing content at scale. If your process touches
multiple data types, the solution usually involves combining model types rather than relying on a single
one.
Off-the-shelf tools are trained on general data, not your domain, your documents, or your
workflows. Custom generative AI is integrated into your systems, fine-tuned on your context, and engineered
to produce outputs your team can actually act on. Most companies using Copilot are getting 20% of its
potential because it isn't connected to the right data sources or processes. We close that gap.
We match the model to the problem, not the other way around. For internal knowledge search
and document reasoning, we use enterprise LLMs (OpenAI, Azure AI, Llama). For automating visual content at
scale, text-to-image models like DALL-E or Stable Diffusion. For voice-driven workflows and conversational
interfaces, speech models including Voicebox and AudioPaLM. For engineering teams needing faster delivery,
code generation models like GitHub Copilot, Codex, and StarCoder. During discovery, we map your
highest-value use cases to the model type that fits, not the one that's trending.
Focused use cases- document generation, intake automation, code review, content workflows
typically show measurable results within 6–10 weeks of deployment. Broader transformation engagements run
3–6 months. Outcomes we consistently see: reduced time on manual knowledge work (40–70%), faster decision
cycles, lower error rates on repetitive classification tasks, and engineering teams shipping faster with
AI-assisted code review. We scope every engagement to a use case where ROI is defensible before we build
anything.
Ready to Build
Talk to our Gen AI team
Free 30-minute discovery call. We'll scope your use case, identify quick wins, and tell you honestly whether Gen AI is the right fit.
No commitment required
US-based team responds in 2 business hours
Performance-based engagement options available
