AI Agents
Systems that complete work, not just answer questions.
AI & Automation
AI that shows up in the P&L
Most AI pilots never reach production because they were never tied to a number. We start from the number — cost, cycle time, conversion, capacity — and build the shortest system that moves it.
- Engagement
- Assessment then phased delivery
- Typical timeline
- 6 weeks to first use case
- Delivered across
- All 50 US states
Overview
Every American business has now been told that AI will transform it. Far fewer have been told which process, by how much, and what it costs to run. That gap is where budgets disappear. Our engagements begin with a use-case assessment that ranks candidate applications by value, feasibility and risk, and openly names the ones not worth doing.
From there we build. Retrieval systems over your own documents, classification and extraction that removes manual review, forecasting that improves planning, agents that complete multi-step work with a human checkpoint where it matters. Each ships with evaluation harnesses so quality is measured rather than assumed, and with guardrails, logging and cost controls so it can run in a regulated US environment.
We are model-agnostic and stay that way deliberately. The frontier moves every few months; systems built around a single vendor's API surface age badly. We design an abstraction layer so you can change models when the price or capability changes — which it will.
- Typical cycle-time reduction on targeted workflows
- 30–60%Typical cycle-time reduction on targeted workflows
- Assessment to first production use case
- 6 weeksAssessment to first production use case
- Decisions logged and auditable
- 100%Decisions logged and auditable
Business benefits
What ai solutions changes for you
The reasons this work earns its budget, stated as outcomes rather than features.
Use cases ranked before code
A scored inventory of candidate applications with expected value, data readiness, effort and risk — so investment goes where the return is.
Evaluation before deployment
Golden datasets and automated scoring tell you how accurate the system is, and warn you when a model or prompt change degrades it.
Human oversight by design
Approval steps, confidence thresholds and escalation paths so AI drafts and humans decide wherever the stakes justify it.
Governance US regulators expect
Data handling, retention, PII redaction, audit logging and model documentation aligned to the NIST AI Risk Management Framework.
Cost that stays predictable
Token budgeting, caching, model routing and spend alerting, so a successful rollout does not produce a surprise invoice.
Portable across models
An abstraction layer over providers means switching from one frontier model to another is a configuration change, not a rebuild.
Problems solved
If any of this sounds familiar
These are the situations clients describe in the first conversation, and what we do about each.
“We ran a pilot and it never reached production.”
Usually a missing operational layer — evaluation, monitoring, error handling, ownership. We productionize what works and retire what does not.
“The model makes things up.”
Retrieval grounding against your own sources, citation requirements, confidence thresholds and refusal behavior when the answer is not in the corpus.
“Legal and compliance blocked the rollout.”
We bring them in during design: data flow documentation, PII handling, vendor terms, retention policy and an audit trail they can inspect.
“Nobody can tell whether the AI is actually working.”
Baseline the process before launch, define quality metrics, and report against them monthly alongside cost per transaction.
Our process
How we deliver it
Each stage has a defined output, so you always know what you are getting and when.
- 01
Opportunity assessment
Process interviews and data review producing a ranked, costed use-case inventory with a recommended starting point.
- 02
Proof of value
A four-to-six week build against real data with measured accuracy, so the go/no-go decision rests on evidence.
- 03
Production engineering
Integration, guardrails, monitoring, cost controls and the human-in-the-loop workflow the process actually requires.
- 04
Rollout and enablement
Phased deployment, training on where to trust and verify the system, and a feedback channel that feeds the improvement loop.
- 05
Operate and improve
Ongoing evaluation, drift monitoring, prompt and model updates, and quarterly review of the next use case in the queue.
Technologies used
The tools behind the work
Chosen for maintainability and hiring depth rather than novelty. We will justify any choice on request, and we avoid technology that makes you dependent on us.
- OpenAI
- Anthropic Claude
- Azure OpenAI
- AWS Bedrock
- LangGraph
- Pinecone
- pgvector
- Python
- TypeScript
- LangSmith
- Modal
Industries served
Where this work lands most often
Sector context changes what good looks like. These are the industries where we have delivered this service repeatedly.
Why Mova
What working with us on ai solutions is like
The same commitments apply to every engagement, regardless of size or service.
A defined first step
We scope a fixed-price starting point so you can evaluate us on ai solutions before committing to a program.
Senior people, named
The team you meet is the team that delivers. You will know exactly who is accountable.
You own the output
Code, accounts, files and documentation are yours from day one, with no lock-in of any kind.
Measured, then reported
We baseline before starting and report against it honestly — including the months that fall short.
Questions
AI Solutions: common questions
The questions we are asked most about ai solutions, answered directly.
With a high-volume, rules-light, text-heavy process where errors are recoverable — document intake, support triage, sales research, quality review. Those deliver measurable value in weeks and build the organizational muscle for harder use cases.
Keep exploring
Related services
Generative AI Development
Content, code and creative systems built on frontier models.
Machine Learning Solutions
Prediction, classification and forecasting on your own data.
AI Automation
Automating the work that was too messy to automate before.
Digital Transformation
Change programs that survive contact with the organization.
Next step
Ready to talk about ai solutions?
Thirty minutes with someone who has delivered this work. We will tell you what it would take, roughly what it would cost, and whether Mova is the right fit.
No pitch deck. A 30-minute conversation about what you are trying to achieve.