01
Match the method to the business question
Raleigh’s software, manufacturing and life-sciences ecosystem creates potential uses for document retrieval, operational classification and decision support. That market context is not evidence that every workflow needs an AI model. A rules engine or better search may solve a well-defined problem with less uncertainty.
For a document assistant, define which sources it may use and who may see each document. For a classifier, define the labels and the cost of false positives and false negatives. For a forecast, identify the business decision, time horizon and data available at prediction time. Those distinctions determine both the implementation and the success measure.
02
Assess data readiness before model selection
Inventory ownership, permitted uses, freshness and missing data. A proof of concept built with clean demonstration records may not represent the live workflow. Include incomplete records, confusing language and unusual but consequential cases in the evaluation set, with authorised access to any sensitive material.
For retrieval-based systems, document ingestion, access control, citations and stale-source behaviour matter as much as the language model. Test questions that should not be answered. A system should be able to show a source, report uncertainty or escalate instead of inventing an answer when evidence is missing.
- Set a non-AI baseline and compare on the same examples.
- Separate evaluation records from examples used to tune the system.
- Define acceptable failure rates and who reviews disputed outputs.
03
Make evaluation a delivery artifact
A scoped pilot should deliver an evaluation dataset, scoring method, results and a record of failure categories. Report quality alongside latency, model usage and human review effort. A demo that answers a few selected questions is not sufficient evidence to approve production deployment.
For an example operations assistant, acceptance might require retrieving the correct approved procedure, preserving document permissions and escalating ambiguous queries. Exact thresholds belong to the agreed business risk and test set; we do not publish invented accuracy percentages or promise that an AI system will be error-free.
04
Control the production boundary
Decide which outputs are advisory and which can trigger an action. Log relevant inputs, source versions, model settings and outcomes according to an agreed retention policy. Changes to a prompt, source corpus or model version should be evaluated before promotion. Monitoring should surface failures and cost changes rather than only uptime.
Codazz serves Raleigh remotely from Edmonton and Chandigarh. We confirm data access and residency restrictions before a pilot. Clinical, regulated or safety-critical use cases need qualified specialist review; software engineering does not establish medical suitability, legal compliance or certification.
Questions before you hire
Should we use AI or conventional automation?
Start with the variability of the task and a non-AI baseline. Rules often suit deterministic workflows; models may help with ambiguous language or patterns. A pilot should compare outcomes, cost and risk.
Do we need to train our own language model?
Not necessarily. Existing models with controlled retrieval may meet the need. Custom training is considered only after data rights, baseline quality and operational requirements are understood.
Can AI access confidential company documents?
Only under agreed access, provider and retention arrangements. Permissions must be enforced during retrieval, and evaluation should include attempts to access material outside the user’s authorisation.
What separates a pilot from a production AI system?
Production requires tested permissions, evaluation and release processes, monitoring, fallback behaviour and an operational owner. A successful demonstration alone does not establish production readiness.
Raleigh project planning resources
Choosing a Raleigh software partner: brief, scorecard and handoffRead the guide →
Planning a Raleigh SaaS MVP: scope, budget model and release gatesRead the guide →Market context comes from the sources below. The workflow examples on these pages are illustrative, not local client case studies.
- Wake County Economic Development: Raleigh’s advanced industries (2025)
- Research Triangle Park: company directory
Content updated . Codazz serves clients worldwide; this is a US city campaign.