AI & Agentic AI Solutions
From pilot to production: AI that does real work.
Who it is for
Operations, product, and IT leaders at growing companies who want AI beyond chat demos: measurable time saved, faster customer response, and new product capability, without betting the company on an unproven vendor.
Most companies have run an AI demo. Far fewer have an AI system in production that saves hours every week, answers from their own data, and can be trusted. We design, build, and harden that second kind.
Deliverables
AI opportunity assessment
A ranked list of use cases scored by value, feasibility, data readiness, and risk, with a 6 to 12 month roadmap.
Knowledge assistants (RAG)
Retrieval-augmented generation over your documents, tickets, contracts, and wikis, with permissions respected and citations shown.
Agentic workflows
Multi-step agents that use tools, call your systems, and route to a human for approval when the stakes are high.
LLM application development
Production apps on Claude, Gemini, OpenAI, or open-weight models, chosen by fit and cost rather than hype.
Evaluation and quality monitoring
Test sets, automated evals, and dashboards so you know when quality drifts before customers do.
Model and vendor selection
Cost modeling, data-handling review, and vendor comparisons so procurement and security sign off quickly.
Engagement approach
Discover
Two weeks with your teams to map workflows, inspect data, and pick the use case with the clearest payoff.
Prototype
A working prototype on real data in two to four weeks, tested by the people who will use it.
Harden
Evals, guardrails, security review, observability, and cost controls before anything touches customers.
Scale
Rollout, training, playbooks, and a hand-off your team can own, or ongoing support if you prefer.
What you walk away with
- Hours per week returned to staff on document-heavy and repetitive work
- Faster, more consistent customer and employee support answers
- A clear AI roadmap your leadership team understands and funds
- Production AI with monitoring, guardrails, and a known cost per task
- Claude
- Gemini
- Vertex AI
- OpenAI
- LangGraph
- Vector search
- BigQuery
- Cloud Run
Common questions
How do you keep our data private?
We prefer architectures where your data stays in your cloud project, use enterprise API terms that exclude training on your data, and enforce document-level permissions inside retrieval. Every engagement includes a data-handling review.
Which model should we use?
It depends on the task. We run your real examples through several candidates and compare quality, latency, and cost before recommending one. Many solutions use a strong model for hard steps and a cheaper one for routine steps.
Do we need a data science team?
No. Most business AI systems are engineering and workflow problems more than modeling problems. We build so your existing developers or IT team can maintain the result.
Often combined with
AI Security
Threat modeling, prompt-injection testing, guardrails, and governance so you can ship AI features without new attack surface.
Learn moreBigQuery & Data Warehousing
Warehouse design, ELT pipelines, cost and performance tuning, and migrations onto BigQuery.
Learn moreDigital Transformation & Process Automation
Process mapping, workflow automation, system integration, and legacy modernization that removes manual work.
Learn moreTalk to us about AI & Agentic AI Solutions
A 30-minute call is enough to tell whether this is the right engagement and what it would take.