AI Harness
A control layer that defines what AI can access, where human approval is required, and checks outputs before they reach production workflows.
Artificial intelligence development services for enterprise teams
AI development that proves its value on your own data in 4–6 weeks. Then scales only what worked.
Most AI budgets are spent before anyone knows whether the use case pays back. We start with a focused pilot on your real business data with clear KPIs, acceptance criteria and an ROI target, then build the production system around evaluation, approvals and audit trails.
- Proof before the big budget. Each pilot carries an ROI target set before development expands, and a decision gate at the end of every stage.
- Production-grade, not a demo. Bias and hallucination audits, evaluation suites and red-team exercises run during the pilot, not after go-live.
- You stay in control. High-risk actions require human approval, sensitive data stays inside defined boundaries, and every component leaves an audit trail.
- Inference costs planned early. Right-sized models, caching and routing of simpler queries to lighter models before usage scales.
5.0Clutch rating, 45 reviews
100+certified engineers
98%client recommendation rate
12+industries served
Results from Devox AI projects
Measured results from AI in real operations
−40%customer support maintenance costsUS telecom, 1M+ inquiries per month
−80%manual effort on export certificatesDocument AI across 34 countries
3xlead processing capacity without proportional headcount growthAI-assisted B2B lead generation
52% → 83%employee survey engagementAI survey engine for HR SaaS
How we de-risk your AI budget
Three stages. A decision gate after each one.
Each stage ends with a written result your finance, security and risk teams can review before you decide on the next one.
14 days · enterprise 4 weeks
AI readiness assessment
Every use case gets a revenue or cost hypothesis and a success metric, or it is removed from the shortlist.
- Use cases ranked by feasibility and payback
- Inference cost model at your expected volume
- Scope for the first pilot
You get: a signed written report and an executive readout call.
4–6 weeks
Pilot on real data
A focused pilot for one high-value problem, such as a copilot, an assistant or a governed workflow.
- Clear KPIs and acceptance criteria
- Performance benchmarks against your current baseline
- Stress tests, bias and hallucination audits
You get: proof before you commit to a full rollout.
Production
Scale what proved value
Enterprise-grade retrieval, role-based access and full LLMOps and MLOps in day-to-day operations.
- Versioning and controlled model updates
- Promotion only after automated checks and human review
- Drift detection with retraining triggers
You get: a system your team can monitor, govern and improve.
Talk through your use case 30 minutes on Zoom, no cost
Two ways an AI project ends
The demo is not the hard part. Everything after it is.
A prototype that answers well in a meeting and a system that can be trusted with customer-facing decisions are different pieces of engineering.
Prototype that stalls
Impressive in the demo, stuck before production
- ✕Quality is judged by how the answers feel, with no evaluation set to measure against.
- ✕Nobody can say what it costs per task once real volume arrives.
- ✕Security and legal see it for the first time when it is ready to launch.
- ✕There is no boundary on what the model is allowed to do on its own.
- ✕When the model is updated, nobody can prove the behavior did not change.
Pilot to production, Devox
Built to survive the review it will get
- ✓A structured evaluation framework with automated metrics, curated test sets and red-team exercises.
- ✓An inference cost model at your expected volume, produced in the assessment.
- ✓Governance in the design: access boundaries, approvals and audit trails from the pilot onward.
- ✓High-risk actions require human approval, and every component leaves a trail your team can review.
- ✓Controlled model updates: promotion only after automated checks and human review, with drift detection.
How the work is contracted
Capped packages instead of one padded number
Work is contracted in short packages of one to three weeks. The pilot itself spans several of them.
Capped work packages
- ✓Each cap is your worst case. You pay actual effort up to it, never above.
- ✓When the work outruns the estimate, the package closes under cap and you keep the difference.
- ✓The next package is priced from the real pace measured on your data and systems.
- ✓Each package ends at a decision gate, so you can stop with a written result in hand.
One fixed price for the AI program
- ✕Padding for uncertainty is baked into the number on day one.
- ✕When models and tooling get faster, that padding stays with the vendor.
- ✕No risk above the cap and all upside below it is exactly what this shape cannot offer.
- ✕Stopping mid-program leaves you with a prototype and no evidence either way.
How we build, and how fast
AI accelerates our delivery too, per task, with a named human checkpoint
We use AI-assisted development throughout delivery, and we measure it per task rather than applying one blanket discount. Overall project gains land around 10 to 30 percent even with near-universal tool adoption, while specific task types genuinely run twice as fast.
| Work type | AI impact | What the human keeps |
|---|---|---|
| Type definitions, scaffolding, configuration | 60–80% faster | Correctness review |
| Converting framework-free business logic | 70–90% faster | Parity test sign-off |
| Simple and static pages | 50–60% faster | Visual QA |
| UI components and state management | 40–50% faster | Design fidelity and accessibility |
| Complex flows and integrations | ~30% faster | Business-logic validation |
| Verifying the live system behaves correctly | ~0% | All of it |
What AI does not replace: architectural decisions, product judgment, cross-browser testing, accessibility review and integration debugging. Every AI-generated output is reviewed and validated by an experienced developer before it enters the codebase. The published cases where AI rewrote a whole system in days had one thing in common, an existing test suite complete enough to judge every change automatically. Where that automatic verdict exists, the work runs at machine pace. Where the verdict is a human looking at a live system, verification sets the clock, and skipping it is paid back in production bugs.
Governance built in
The controls security and legal will ask about
Evaluation
A structured evaluation framework with automated metrics, curated test sets and red-team exercises.
Security
Continuous security validation before release, including code scanning and database access validation.
Cost control
Cost budgets with early-exit signals prevent runaway token spend, so you can track cost per task.
Technology
The full stack behind a production AI system
A model is one layer. What makes it usable in a regulated business is everything around it.
Models and reasoning
ClaudeOpenAI modelsGeminiOpen-weight modelsFine-tuningPrompt and context engineeringMulti-agent orchestrationTool and function calling
Retrieval and data
Retrieval-augmented generationVector searchChunking and re-rankingOCR and document parsingPostgreSQLData lake and warehouseData quality profilingRedaction layers
Application and integration
PythonFastAPI.NET 9 / C#Node.jsTypeScriptReactNext.jsREST and GraphQL APIsEvent-driven workflowsCRM, ERP and ticketing integrations
Evaluation and safety
Evaluation suites with automated metricsCurated test setsBias and hallucination auditsRed-team exercisesHuman-in-the-loop approvalsAudit trailsGuardrails and access boundaries
LLMOps and MLOps
Model and prompt versioningControlled model updatesDrift detection with retraining triggersObservability and tracingToken and cost budgetsCaching and model routing
Infrastructure
AWSAzureGoogle CloudPrivate and on-premise environmentsDockerKubernetesCI/CD pipelinesInfrastructure as code
Where data cannot leave your environment, the system runs in a private or on-premise setup, and redaction layers keep sensitive information inside approved boundaries. The assessment states which parts of the stack your case actually needs, rather than every item on this list.
Case studies
From one use case to measurable results
AI customer support assistant
An AI layer on the existing customer service stack, trained on 50,000+ historical interactions.
- 30%faster resolution of customer issues
- −40%customer support maintenance costs
Document AI for export certificates
AI extraction from invoices, packing lists and transport tables, with confidence scoring for manual review.
- −80%manual effort
- 34countries supported without development overhead
AI-assisted lead generation platform
Automated prospect research, validation and outreach preparation with human-in-the-loop workflows.
- ~65%less manual research and data entry
- +38%outbound engagement rates
FAQ
What leaders ask before funding AI
How fast will we see whether AI works for us?
The pilot runs 4 to 6 weeks on your real business data, with clear KPIs, acceptance criteria and an ROI target set before development expands, so you see proof before committing to a full rollout.
Our data is messy. Can we still start?
Yes. The readiness assessment inventories every significant data source, profiles quality and flags what is usable for AI before a pilot runs, so the pilot is scoped around data that exists rather than data you wish you had.
How do you keep AI from making risky decisions?
High-risk actions require human approval, sensitive data stays within defined access boundaries, and every component creates an audit trail your team can review. A control layer defines what the system is allowed to access and checks outputs before they reach production workflows.
What will it cost to run in production?
The assessment includes an inference cost model at your expected transaction volume. In delivery we right-size models for each task, cache frequent responses and route simpler queries to lighter models, with cost budgets and early-exit signals in place.
How long does an AI agent MVP take?
An agentic AI MVP takes 10 to 16 weeks and ships all 9 layers of the production stack with a 10-metric evaluation suite as a standard deliverable.
What does an AI development company actually deliver here?
Artificial intelligence services cover a wide range, so it is worth being concrete. In practice the work falls into four shapes: an assistant or copilot on your own content, document and data extraction, a governed agent that performs work inside your systems, and machine learning models on your operational data. The AI software development around the model, integration, evaluation, access control and cost management, is usually the larger part, and it is what decides whether AI solutions survive their first quarter in production.
Can we keep the system in our own environment?
Yes. The stack runs in a private or on-premise environment where required, with redaction layers preventing sensitive information from leaving approved boundaries.
What happens when the model we use is replaced by a better one?
Model and prompt versions are managed like code: a new version is promoted only after automated checks and human review, with the evaluation suite proving the behavior did not regress, and drift detection triggering retraining when inputs shift.
Find the AI use case that pays back first
30 minutes on your use case, data and systems, and what a 4–6 week pilot would need to prove before anyone signs off a rollout.
Pick a time Free call on Zoom