05 / AI & machine learning
AI & Machine Learning Consulting
Enterprise AI and machine learning engineering connects predictive models and AI applications to production systems, data access controls and measurable evaluation. Quorlytic scopes a bounded task with a clear failure cost, protects the underlying data, tests failure modes and adversarial inputs, and monitors the system after it deploys.
By QuorlyticUpdated
Build AI around a bounded task, trusted data and measurable acceptance criteria. Connect predictive models and language-model applications to the systems, people and controls that make them usable.
Enterprise data
- Documents
- Tables
- Events
- RetrievalGrounded, permission-aware
- FeaturesPoint-in-time correct
Guardrails — access · policy · PII · output checks
- Model
- Tools / agentsScoped permissions
- ApplicationAnswer with citations
- Human reviewWhere the failure cost is high
- EvaluationAcceptance tests before release
- ObservabilityQuality · latency · cost · drift
The business problem
Define the user, input, permitted output and failure cost. Recommendation, text analysis and enterprise search each need different evaluation methods. Compare the proposed AI system with a simpler alternative before accepting its ongoing cost.
What we build
For retrieval-augmented generation, ingestion and permissions matter as much as model choice. Track document versions, freshness and deletion. Retrieval must enforce access before context reaches the model; a prompt is not an authorization boundary.
The outcome
Task definition and evaluation dataset
When this is the right engagement
Symptoms that bring teams here.
Most enterprise AI work stalls for the same reasons, and none of them are about the model. These are the symptoms.
A pilot demonstrated well and never reached production
Retrieval, permissions and evaluation were treated as follow-up work rather than as the architecture.
The assistant answers confidently and is sometimes wrong
There is no grounding discipline and no evaluation set, so quality is anecdotal.
Nobody can say whether a user should have seen that document
Permissions are enforced at the application but not at retrieval — the most common enterprise AI failure.
Quality is assessed by asking it a few questions
There is no repeatable evaluation, so a prompt or model change cannot be judged.
Costs are unpredictable from month to month
Token consumption is unattributed, so no workload can be identified as expensive.
The answer cannot be traced to a source
Without citation the output cannot be checked, so it cannot be used where being wrong matters.
Capability model
Enterprise AI architecture.
Production AI is an architecture, not a model choice. Select a layer to see what it does and where enterprise deployments fail.
01 — Business workflow
Identify the task AI is inside — who does it, how often, and what happens when the output is wrong.
What it produces
A bounded task with a defined failure behaviour.
What usually goes wrong
- A capability chosen before a task
- No fallback when the model is unavailable or unsure
- Users given no way to correct or escalate
Technologies in this stage
- Task analysis
- Human-in-the-loop design
- Fallback paths
02 — Orchestration
Control the sequence: what is retrieved, what tools may be called, what is passed to the model and what is returned.
What it produces
A versioned, bounded request path with limits and audit.
What usually goes wrong
- Unbounded tool use with no approval step
- Prompt logic scattered across the application
- No versioning, so behaviour changes untraceably
- No timeout or cost ceiling per request
Technologies in this stage
- Orchestration layer
- Tool definitions
- Prompt versioning
- Guardrails
03 — Model
Choose the model for the task, and keep the choice replaceable as capability and pricing move.
What it produces
A task-appropriate model behind an interface you can change.
What usually goes wrong
- Application coupled to one provider's interface
- Model selected on benchmarks rather than on the task
- Context limits discovered in production
- No record of which version produced which output
Technologies in this stage
- LLM APIs
- Open models
- Fine-tuning
- Model routing
04 — Retrieval and tools
Ground answers in enterprise content the user is entitled to see, and return the citation with the answer.
What it produces
Permission-aware retrieval with citations, measured on its own.
What usually goes wrong
- Permissions applied after retrieval instead of within it
- Chunking that severs the context a passage needs
- Stale index with no reindex trigger
- Retrieval quality never measured separately from answer quality
Technologies in this stage
- Vector search
- Hybrid retrieval
- Document processing
- Tool APIs
05 — Enterprise data
Make the underlying content correct, current and classified, because retrieval inherits every weakness beneath it.
What it produces
A governed corpus with ownership and a refresh path.
What usually goes wrong
- Duplicate and superseded documents indexed together
- No classification, so sensitive content cannot be excluded
- Source of truth undefined for contested content
Technologies in this stage
- Content pipelines
- Classification
- Deduplication
- Warehouse models
06 — Evaluation and monitoring
Make quality measurable so a change can be judged, and watch behaviour after release.
What it produces
A repeatable evaluation and live monitoring with a feedback route.
What usually goes wrong
- No evaluation set, so every change is a guess
- Evaluation built only from questions the system already answers well
- Cost and latency unmonitored per workload
- No route for users to flag a wrong answer
Technologies in this stage
- Evaluation sets
- Automated scoring
- Human review
- Cost and latency tracking
Illustrative reference architecture
Reference architecture
An enterprise AI system drawn as the layers that actually decide whether it reaches production. Governance and evaluation run across all of them, which is why they are drawn across rather than appended.
Business workflow
The bounded task, its owner and its failure behaviour.
- Task
- Human in the loop
- Fallback
Application
Where the user meets it, including how they correct it.
- Interface
- Feedback
- Escalation
Orchestration
Sequence, tool permissions, limits and versioning.
- Prompt versions
- Tool policy
- Cost ceiling
- Audit
Model
Chosen per task, kept replaceable.
- LLM APIs
- Open models
- Routing
Retrieval and tools
Permission-aware grounding with citations returned.
- Vector search
- Hybrid search
- Tool APIs
Enterprise data
Governed, classified, current content underneath it all.
- Documents
- Warehouse models
- Classification
Across every layer
- Identity and permissions
- Evaluation
- Citation and traceability
- Cost and latency monitoring
- Data classification
The approach
- 01
Choose a task that can be evaluated
Define the user, input, permitted output and failure cost. Recommendation, text analysis and enterprise search each need different evaluation methods. Compare the proposed AI system with a simpler alternative before accepting its ongoing cost.
- 02
Treat retrieval as a data product
For retrieval-augmented generation, ingestion and permissions matter as much as model choice. Track document versions, freshness and deletion. Retrieval must enforce access before context reaches the model; a prompt is not an authorization boundary.
- Predictive models and recommendation systems
- Enterprise search and RAG applications
- Bounded AI agents with approval and tool-access controls
- 03
Evaluate failure, not just the demo
Use representative task examples, permission tests, unsupported-question tests and adversarial inputs. Measure retrieval quality, grounded answers, latency and cost. Require human approval for actions with material business impact, and define a safe fallback when the system cannot answer.
- 04
Operate the whole system
Version data, models, prompts and evaluation sets. Monitor quality changes after an upstream model or document update. MLOps and LLMOps include incident response, rollback and accountable ownership—not just deploying an endpoint.
- 05
Generative AI needs a task, not a platform choice
Generative AI becomes useful when it is attached to a specific job: drafting against approved sources, summarising a defined corpus, extracting structured fields from documents, or answering questions within a permission boundary. Choosing a model or a platform before naming that job produces a demonstration rather than a system.
- The job, its consumer and its failure cost written down
- A simpler non-generative option compared first
- Model and vendor chosen last, against the task
- 06
Agents need permissions, limits and an audit trail
An agent that can call tools is a system with authority, so the authority has to be designed. Give it the smallest useful set of tools, require approval for irreversible actions, log what it did and on whose behalf, and define what happens when it is wrong. Read-only retrieval is the right starting scope for most first deployments.
- Tool access scoped and reviewed like any other permission
- Irreversible actions require human approval
- Every consequential action recorded with its inputs
- 07
Machine learning still needs release discipline
Predictive models change behaviour when upstream data, features or populations move. Version the training data, features, code and evaluation set together, promote models through a reviewed path, and keep the previous version available for rollback. Monitoring covers input drift and decision outcomes, not only service availability.
- Training data, features and evaluation set versioned together
- Promotion and rollback path defined before launch
- Drift and outcome monitoring owned by a named team
Delivery model
How the work is sequenced.
Sequenced so the evaluation exists before the system does, which is the only way a change can be judged.
| Phase | Activities | Decisions made here | What you keep |
|---|---|---|---|
| AssessFind a task worth automating and check the data can support it. |
|
| Bounded use case and a data readiness assessment |
| ArchitectDesign grounding, permissions and evaluation together. |
|
| Architecture, evaluation set and guardrail definition |
| BuildImplement and measure against the set. |
|
| Grounded system with evaluation results and citations |
| OperateWatch it in production and keep improving it. |
|
| Monitoring, feedback triage and a change-evaluation process |
Technology fit
What each technology is here to do.
Roles in an enterprise AI stack. The interesting decisions are in retrieval and evaluation, not in model selection.
Model layer
- LLM APIs
- Open models
- Fine-tuning
- Embeddings
Selected per task and kept behind an interface, because capability and price move faster than any application can be rewritten.
Retrieval
- Vector search
- Hybrid search
- Document processing
- Rerankers
The layer that decides whether an answer is grounded. It is also where permissions have to be enforced.
Orchestration controls
- Tool definitions
- Prompt versioning
- Guardrails
- Routing
Bounds what the system may do and makes behaviour reproducible and auditable.
Data foundation
- Content pipelines
- Classification
- Warehouse models
- Deduplication
Retrieval cannot be better than the corpus beneath it.
Platform detailOperations
- Evaluation sets
- Automated scoring
- Cost tracking
- Feedback capture
How quality becomes measurable rather than anecdotal, before and after release.
Platform detail
Technology marks belong to their respective owners. No partnership, certification or endorsement is implied.
Engineering concerns
What has to be answered before it is trusted.
The production concerns that decide whether an enterprise AI system is deployable.
- Permission-aware retrieval
- Access enforced at retrieval so a user cannot be shown content their identity does not entitle them to, including through a summary.
- Traceability
- Every answer carries its sources, so it can be checked by the person relying on it.
- Evaluation
- A held-out set of real questions with expected behaviour, re-run on every prompt, model or index change.
- Autonomy limits
- Tool use bounded explicitly, with approval steps for anything that writes, sends or spends.
- Data classification
- Sensitive content identified before indexing, since exclusion is far cheaper than retraction.
- Cost and latency
- Token use and response time attributed per workload, so an expensive feature is a visible decision.
Where it applies
Related work, and why it is related.
Where enterprise AI has a bounded, checkable task, and what it connects to.
- Enterprise AI searchThe most common production-ready case: bounded scope, permission-aware retrieval and citations that make the answer checkable.
- Customer 360Resolved identity is what lets an assistant answer about a customer rather than about a document.
- HealthcareClassification and permission enforcement are the architecture, not a compliance review at the end.
- SaaSProduct-embedded assistants where per-tenant isolation has to hold at the retrieval layer.
- Managed data and AIEvaluation, cost monitoring and incident response once the system is live.
Make the acceptance decision specific to the release.
A convincing demonstration does not identify which change is safe to release. In this hypothetical support assistant, the team proposes a shorter prompt while keeping the task read-only. The acceptance record connects that exact candidate to representative questions, permission boundaries and a recoverable operating state. It leaves room for the decision to be no change.
Scope and assumptions: The task owner defines acceptable answers and failure costs; the application and security owners define the access and action boundaries. This is an example of evidence to request, not a completed Quorlytic evaluation, a promised accuracy level or approval to run an agent against business systems.
| Dimension | Candidate and decision | Evidence needed before acceptance |
|---|---|---|
| Exact candidate | Record the prompt revision, model identifier, corpus snapshot, retrieval settings and permission policy. Name what changes and what is held constant. | Keep the previous working configuration and the proposed configuration with the same task definition. A screenshot or an unversioned demo cannot identify the candidate being accepted. |
| Paired questions | Run the current and proposed versions on the same held-out questions. Include ordinary requests, missing evidence, conflicting sources and attempts to obtain material outside the reader's scope. | Retain the input, permitted evidence, expected outcome and actual answer for each case. Keep answer support, refusal behavior, latency and cost separate rather than hiding a failure behind one average. |
| Access and actions | The assistant may read approved support material and draft an answer; it may not change an account. A request to perform a write must remain outside that scope even if the generated response looks reasonable. | Inspect the actual request path and tool policy, not only the final text. OpenAI's Agents SDK distinguishes input, output and tool checks; a final output check is not proof that no tool already ran. |
| Disputed grades | Agree a rubric with the people who own the task. Where an automated judge and a subject expert disagree, retain the disputed case and the reason for the final decision. | Check both accepted and rejected examples against human judgments. A judge that routinely passes answers is not useful evidence that the candidate handles important failures. |
| Release and recovery | Define who can accept the change, which observations require a stop, and what can actually be restored. Reverting a prompt does not restore a deleted source or reverse a permission change. | Keep the release decision with the evaluated component versions and the recovery procedure. Recheck the current corpus and entitlements after recovery; do not restore withdrawn access to reproduce an old score. |
At narrow widths, scroll the worksheet to read every column. With the table focused, use the arrow keys to scroll.
Worked decision: the shorter prompt improves response time but answers one restricted-source question from remembered conversation context. The access failure remains visible even if most answers improve. Keep the existing release while the context path is corrected, then rerun the affected cases and the agreed regression set. No benchmark winner or measured result is asserted by this example.
- Separate the engineering evidence from the business decision. A passing technical run does not choose an acceptable failure cost or establish permission for a new action.
- Keep real support content and personal information out of broadly shared traces. Evaluation evidence needs access and retention rules of its own, and useful failure cases should survive as controlled regression fixtures.
If the remaining failure is being attributed to the wrong technique, use the controlled retrieval and tuning comparison before adding another model change.
Official references for this example:
Questions
Questions worth asking.
Should we start with an AI agent?
Start with the task. A search interface or deterministic workflow may be sufficient. Add autonomous tool use only when its benefit justifies permissions, approval, audit and recovery complexity.
Can company data stay in our environment?
Deployment and data-flow options depend on the platform and model. Establish residency, retention, access and vendor-processing requirements before selecting an architecture.
Do we need an agent or a search interface?
Start with search and retrieval, which answer questions without taking action. Move to an agent only when the task genuinely requires calling a tool, the benefit justifies the access it needs, and there is a clear approval path for anything irreversible. Most first deployments do not need autonomy.
Do you build RAG and generative AI applications?
Yes, for bounded tasks: retrieval over your own documents with access controls enforced at query time, answers that cite their sources, an evaluation set that includes questions the system should refuse, and a fallback when it cannot answer. We start with one well-defined workflow rather than a general-purpose assistant.
Next step
Assess your AI readiness.
Bring the workflow you want AI inside, and the data it would have to read.