Turn AI into a business capability
not a pilot project.
Most AI initiatives stall at the prototype. KPThink helps enterprises design, build, and operate AI systems that affect real outcomes, from ML pipelines in production to generative AI embedded in workflows your teams actually use.
AI consulting services
Honestly, most companies don't need more AI experimentation. They need someone to take a working idea into production and keep it there.
AI Strategy & Use Case Design
Most AI roadmaps start with 'deploy an LLM' and never get more specific. We help you work out where AI genuinely moves the needle for your business, and where it adds cost without return. The output is a phased plan tied to real metrics, not a slide deck.
Machine Learning Implementation
Model selection, training pipelines, validation, and deployment on Azure ML or AWS SageMaker. We stay through go-live, not just the proof of concept. An 82% accuracy score in staging means nothing if the model drifts within 60 days of launch.
MLOps & Model Governance
A model that works in a notebook isn't a product. We build CI/CD for ML: drift monitoring, retraining triggers, and audit trails that keep models reliable long after the initial deployment. Your data science team builds; we make sure it stays working.
Generative AI Integration
LLM integration, RAG pipeline design, prompt engineering, and fine-tuning on your proprietary data. Built on Azure OpenAI, AWS Bedrock, or open-source models, chosen based on your compliance requirements, not whichever vendor published the most blog posts this month.
AI products we've shipped
These aren't demos or internal tools. They're production systems. Which is probably why our AI architecture advice tends to be more specific than most.
Enterprise Audio IntelligenceAulixa
Transcription, sentiment, and automatic PII redaction for customer-facing teams.
Aulixa processes call recordings and meeting audio to extract intent, flag frustration patterns, and scrub sensitive data, including credit cards, SSNs, and account numbers, before it reaches your logs. GDPR and PCI-DSS compliant from day one, not patched in afterward.
AI Content & SEO AutomationCiteya
From keyword to published article in under five minutes.
Citeya handles research, writing, metadata, citation sourcing, and CMS publishing inside one platform. Teams that once struggled to keep a weekly publishing schedule now ship consistently, without adding headcount or cutting corners on quality checks.
AI Career AssistantSleevIx
ATS-optimized cover letters and recruiter messages in seconds.
SleevIx reads a resume against a job description and generates tailored application materials built to clear automated screeners. Used by candidates from fresh graduates to senior executives pivoting roles.
We don't just advise on AI. We build it.
A lot of AI consultants will give you a framework, hand over a report, and leave. That report doesn't deploy itself. KPThink stays through implementation. That's where the actual problems surface.
Start the conversationWe build on your cloud, not around it
Every AI system we deploy runs inside your Azure or AWS environment. No black-box vendor lock-in, no surprise egress costs, no compliance questions you can't answer to your auditor. Your data stays where your contracts say it stays.
We've shipped real AI products ourselves
Aulixa, Citeya, and SleevIx are live products KPThink built and operates. When we advise on AI architecture, we're drawing from direct production experience, not certifications and case study PDFs from someone else's project.
Security and compliance come standard
PII redaction, data governance, audit trails, and model monitoring aren't add-ons here. We've seen what happens when they're treated as afterthoughts. It's usually around the time someone schedules an emergency call on a Friday afternoon.
Frequently Asked Questions
How much does a generative AI or MLOps engagement cost?
Cost depends on scope: a single use-case pilot costs less than a full MLOps platform build. Visit our pricing page for plan details, or talk to our team for a project-specific quote.
How long does it take to get an AI system into production?
Industry-typical timelines run 8 to 16 weeks from scoping to a production pilot, depending on data readiness and integration complexity. Ongoing monitoring and retraining continue after launch.
What is KPThink's process for building AI systems?
We start with AI strategy and use-case design to confirm the work is worth doing, then move into model implementation on Azure ML or AWS SageMaker. MLOps practices like drift monitoring and retraining keep the system reliable after go-live, and generative AI integration is scoped separately when it applies.
What do we need before starting an AI project?
You need a defined business problem, access to the data that would train or ground the model, and a stakeholder who can judge whether the output is actually useful. Without those three, the project stalls at the prototype stage.
Ready to build AI
into your operations?
Let's work out where AI genuinely helps your business, and build it properly. No slide decks. No pilot projects that go nowhere.