We are an AI Development Company for B2B SaaS, fintech, and law firms. We ship LLM, RAG, and ML systems into production, measured in revenue.
AI development problems
AI development services
We ship working systems, not hours of AI expertise. Our AI Development Services cover LLM applications, RAG systems with reranking and refusal handling, custom ML models, computer vision, NLP pipelines, AI features inside your SaaS product, and the MLOps to run it all.
Enterprise AI development is how Konica Minolta certified 2,400+ users on a platform we built and cut training costs 65%. Custom AI solutions for B2B start with your data, not a demo.
Why teams switch
MIT found 95% of GenAI pilots never deliver measurable results. So we work differently. One, paid discovery before any build quote, no exceptions. Two, evals run on every commit, with weekly human review.
Three, AI product engineering and marketing sit under one roof, so we ship the system and the demand that fills it. Four, month-to-month terms with named engineers and 30-day notice. If the AI stops earning its keep, you can leave.
The first 90 days
Days 1 to 14: paid discovery locks the use case, data-readiness report, eval plan, and a fixed-price quote for module one.
Days 15 to 45: module one hits staging with its eval suite live.
Days 46 to 75: module one runs in production with pilot users and an adoption dashboard.
Days 76 to 90: module two ships, and your CFO gets a readout with a revenue or cost-saved number. LLM and RAG development on a calendar, not a promise.
Evals and guardrails
Every use case gets a golden dataset, zero-error zones defined upfront, and eval gates that block deploys that fail. Guardrails cover PII redaction, output filters, and refusal paths, aligned to NIST SP 800-218A. Every prompt, retrieval, and token cost is traced.
Then we report what a CFO cares about: adoption on shipped features, cost per successful task, and revenue tied to the AI. That is how Worldpay read $7.9M in pipeline from one build.
Enterprise AI results
One finds the leaks in your stack. The other proves the AI in 30 days.
An AI rep that books meetings and proves itself in 30 days.
AI development process
Step 01. Strategize
We define the one job the AI must do, the data it needs, and the number that proves it worked. Paid discovery locks the spec.
Step 02. Execute
Module one moves from staging to production with evals on every commit, guardrails wired, and weekly sprint reviews.
Count the meetings.
Every prompt, retrieval, and output gets traced. An adoption dashboard ties the AI to revenue or cost saved, in numbers your CFO reads.
Step 04. Accelerate
Budget follows what works. We scale the modules that produce, retire the ones that do not, and review it with you weekly.
AI project fit criteria
Good fit: B2B companies from $2M to $100M in revenue with a live product or ops process AI can move, and budgets from $75K to $500K.
Not a fit: pre-revenue AI wrappers, projects with no data owner, or a POC with no production plan.
Questions buyers ask on the first call
AI development resources
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We can show production systems with adoption numbers, not demo reels. Talk before they choose.
