Technical Specification for a Corporate AI Platform: Structure of Requirements

Why you need a technical specification and what it should capture

A technical specification is not a formality. It records which problem the AI platform solves, who its users are and what a good outcome looks like. Without this, the team can quickly build an “impressive demo” that won’t help in real work.

For a corporate system it’s important to agree boundaries in advance: which processes are affected, which data can be used, where AI provides a recommendation and where it makes decisions. The same tool behaves differently for support, legal, HR or production. The specification helps avoid mixing expectations and prevents disputes months later.

At the start teams often forget things that later determine success: what counts as a “correct answer” and who validates quality; which errors are unacceptable and what to do in doubtful cases (fail‑safe rule and manual review); latency requirements (how many seconds are acceptable in each scenario); how users provide feedback and how it flows into improvements; which reports and logs are required for audits and incident investigations.

Example: a bank deploys an assistant for contact center agents. If the spec doesn’t mandate that responses rely on internal regulations, use only approved knowledge bases and offer a "suggestion only" mode, the system will start to confidently "hallucinate" and increase risk.

The specification defines goals, metrics, constraints and acceptance. Detailed design is usually left to the implementation team: specific models and vendors, deployment architecture, monitoring tools and integration approaches.

Goals, scope and users

Goals in the specification are needed to fairly accept the work later. Phrase them measurably: which quality metric is required, how long a response may take, and what coverage of users or processes the first release should include.

Examples: “at least 85% accuracy on a test set of N cases”, “response time under 3 seconds for 95% of requests”, “cover 2 departments and 5 typical scenarios within 3 months”. This makes the spec verifiable and reduces disputes.

Next, fix the scope. Which scenarios are in scope (e.g., search across internal documents, ticket classification, operator suggestions) and which are out of scope (generating legal opinions, automatic financial decisions, handling personal data outside an isolated environment). Scope matters because AI tends to expand with expectations.

Describe users by roles and tasks. Usually three profiles are sufficient: operator (quickly gets suggestions, sees sources, confirms or rejects results), analyst (tunes evaluation rules, reviews quality reports, proposes improvements) and administrator (manages access, integrations, logs and incidents).

Finally, add constraints: timelines and budget, mandatory regulator and internal security requirements, supported languages (e.g., Russian and Kazakh), and places where a human must be in the loop (confirming decisions, reviewing sensitive responses).

Roles, responsibilities and change control

To prevent the specification becoming a wishlist, define who makes decisions and who is accountable. Start with stakeholders: business owner (for impact), IT (for integrations and infrastructure), InfoSec/Compliance (for risks and access), data owner (for sources and quality), legal (for contractual limits), and support (for operation). If a contractor or integrator is involved, describe their role too.

Specify who approves key decisions: case selection and metrics, data approval for use, user access levels, delivery model (on‑prem or cloud), criteria for pilot readiness and production readiness.

RACI for key tasks

Prepare a simple RACI matrix for common failure areas. Typical entries:

• Data (source connection, quality, rights): R - data owner, A - business owner, C - IT/InfoSec, I - users
• Security and access: R - InfoSec, A - CISO/head of security, C - IT/legal, I - business
• Operation and SLA: R - IT/support, A - IT director, C - contractor, I - business
• Model quality and metrics: R - DS/ML team, A - product owner, C - business experts, I - IT
• Changes and releases: R - product manager, A - change committee, C - IT/InfoSec, I - all affected

Change control and versioning

Describe how specification edits are requested, who evaluates impact (time, budget, risks), who approves, and how document versions are tracked. Minimum: version number, date, author, change list, status (draft/under review/approved).

Separate artifacts by stage. For a pilot, you typically need a case description, metrics, list of data and access, integration prototype, test plan and rollback plan. For production add threat model, support procedures, quality and drift monitoring, user instructions, change log, training plan and formal acceptance criteria.

Model requirements and output quality

Don’t just write "AI required"—list concrete tasks: ticket/document classification, knowledge base search, an assistant for staff, forecasting (demand or load), data extraction from scans.

Describe quality quantitatively. For each task set metrics and minimum thresholds, and define the cost of errors. For classification use precision/recall and share of “uncertain” answers; for search—share of relevant items in top‑N; for assistants—share of correct answers on a control set and hallucination rate. Separate critical errors (e.g., wrong regulatory recommendation) from acceptable ones if marked as “needs clarification.”

Verifiability matters more than a flashy demo. Require explainability: links to sources in the knowledge base, highlighting fragments that influenced the answer, logging model version and inputs. Define the fail‑safe rule: when the model should respond “I don’t know” and escalate to a human.

Performance and inference cost should be as strict as quality. Specify target response time (e.g., P95 ≤ X seconds) for chat and search, maximum load during peak hours, CPU/GPU limits and inference budget, stability requirements (share of 5xx errors, behavior under load), and data update latency for search indexes.

Also set an update policy: how often to retrain, conditions for releasing a new version and who signs off. A useful practice is “quality gates”: a model is promoted to production only if it passes a test set, does not degrade key metrics and has a rollback plan. In sensitive contexts (bank, government) releases are typically approved by the process owner, security and ops teams.

Data: sources, quality, labeling and storage

Data must be described as strictly as functionality. If you don’t define sources, owners and formats in advance, model quality will fluctuate and schedules will slip.

List sources: CRM/ERP, document management, Service Desk, email, knowledge bases, files, sensors, call center. For each source specify owner (who authorizes access and confirms field meanings), format (tables, text, PDF, audio), volume and update frequency (real‑time, hourly, daily). Record restrictions: what cannot be exported, which fields are dirty, and which reference tables are authoritative.

Then set quality rules. Describe mandatory checks before training and before launch: completeness, missing value rates, duplicates, invalid dates, broken encodings, mismatch with reference tables. It’s useful to state thresholds and actions on deviations (cleaning, excluding rows, requesting fixes from the owner).

Labeling is often a bottleneck. Specify who labels (experts, operators, contractor), where instructions are stored, how disputes are resolved and how labeling quality is verified (double‑label a portion, spot checks, agreement metrics).

For storage and access define where raw, cleaned and labeled datasets reside and who can access each level; retention and deletion rules, backups, access audit (who exported what and when); and dataset versioning so results are reproducible.

If personal or sensitive data is involved, apply data minimization (only collect needed fields). Describe anonymization (masking names, phones, IDs), approvals from security and legal, and prohibit use of such data in test environments without explicit permission.

Example: for a support assistant start with Service Desk tickets and the knowledge base, but specify in the spec that phone numbers and personal data are removed before entering the training set and exports are available only to the project team with an audit trail.

Integrations: how AI fits into processes

Integrations answer where AI gets data and where results return so it becomes part of work rather than a separate showcase. Describe integrations by process: contract approval, ticket handling, knowledge search, call quality control.

List systems involved in the chain: ERP/CRM, document management, corporate mail and calendar, call center, ticketing portal, file storage, corporate databases. For each system record the owner, environment (test or prod) and constraints (access only from internal network or via proxy).

How to connect and when to invoke

Choose a primary integration method and a fallback to avoid blockers during rollout. Common combinations are APIs for online requests, message queues for async tasks and batch file exchange. If the company has an ESB or event streams, include them as an option.

Describe events and triggers: what starts the AI (new ticket created, email received, document status changed), which parameters are passed and where the response should appear (CRM field, ticket comment, document attachment, task for a responsible person).

Reliability, errors and observability

Integrations must degrade gracefully. Specify timeouts, retries, idempotency, retry queues and a degradation scenario: if AI is unavailable the process continues with default templates and no automatic suggestions.

Define exchange formats (JSON, XML, CSV), field schemas, encodings, versioning and masking of sensitive data. For logs state what to record: request ID, source, timestamp, status, model version, error code. Do not store extra content (e.g., full email body) if policies forbid it.

Example: in a call center the event “call finished” sends metadata and a transcript to a queue, AI returns categories and risk flags to the CRM, and on failure opens a manual review task and logs the integration error.

Security and compliance

Describe security through a threat model: what you protect, from whom and by which controls. For AI this includes data leaks, poisoning of training sets, attacks on integrations and prompt injections that try to make the model reveal secrets or perform undesired actions.

Start with boundaries: which data can be sent to AI, which cannot, and where environments live (internal network, DMZ, cloud, isolated contour). Define what is sensitive in your context: personal data, trade secrets, medical records, procurement documents.

Assign access by role rather than broadly. The spec usually defines roles (user, administrator, data owner, MLOps, auditor), MFA for privileged operations, least privilege, separation of access to data, models and logs, and service accounts for integrations instead of shared passwords.

Specify encryption at rest and in transit. Clarify key management, rotation and incident handling. If secrets must be stored only in approved vaults, state that explicitly.

Logging is needed for incident analysis and audit. Define which events are logged (inputs, model calls, prompt/policy changes, data access, errors), retention period, storage location and who can read logs. For example, auditors may need access to events but not to sensitive request contents—so plan masking and access segregation.

Finally add compliance requirements: internal security policies, regulator obligations, incident response procedures and regular review cadence.

Operation: MLOps, monitoring, support and SLA

A strong model loses value quickly without a plan for production life. If MLOps, monitoring and support are not defined, post‑launch maintenance falls into manual fixes and blame games.

Environments and releases

You typically need at least three environments: development, test and production. In the spec, describe differences: access, datasets (anonymized or synthetic for test), resource limits, and who and how moves models and configs between environments.

Describe release steps simply: release readiness criteria (tests, quality metrics, security checks), change windows and approvals, rollback plan and change log.

Monitoring, incidents and SLA

Monitoring should cover availability and quality. Specify mandatory metrics: accuracy on a control sample, data drift signals, failure rate, response time and infrastructure load. For alerts state thresholds, notification channels and who acknowledges incidents.

SLA and support should be clear: hours of operation and support levels (1st, 2nd, 3rd line), response and recovery times for critical and non‑critical failures, escalation rules and decision owners, a knowledge base and runbooks.

Also define backup and recovery: what is backed up (data, models, configs), frequency, storage location, RPO/RTO and responsible parties.

If the AI service runs on your own servers, determine who provides 24/7 response and how recovery is tested. This often involves 24/7 processes and system integrator networks, such as GSE.kz, but these requirements must be specified in the document.

Step‑by‑step structure: how to collect requirements without gaps

A practical spec is built in stages: first record how people actually work, then check data, then agree on quality, and only afterward finalize integrations, security and operations.

Recommended order:

1. Collect scenarios and sample queries. Ask each user type to provide 10–20 real phrasings (how they write in emails, chats, tickets). Specify what counts as a “correct answer” and the required format (text, table, document link, short summary).

2. Describe data and verify availability. For each scenario list source systems, data owner, update frequency, fields, quality and extraction restrictions. A common problem: needed data exists but is legally or technically unavailable.

3. Choose metrics and acceptance thresholds. Metrics should be business‑friendly: accuracy, recall, share of “wrong recommendations”, response time, share of requests sent to manual review. Agree thresholds and how measurements are taken (which dataset and what period).

4. Agree integrations and security contours. Record where AI will run (internal network, isolated segment), authentication, logging, which data cannot be sent to the model and who has admin rights.

5. Describe operation and training, then finalize checks. Define support roles, quality monitoring, model update process, change windows, SLA and a user training plan (short guidelines, examples, typical errors).

Example for a bank contact center: scenarios provide real dialogs, data owners confirm access to knowledge bases and CRM, metrics fix a reduction in response time without more errors, integrations describe operator UI behavior, and security forbids personal data in logs. This walkthrough quickly shows what to add to the specification before pilot launch.

User training and rules for working with AI

A capable model still performs poorly if people don’t know how to use it. Training should be documented like functionality: who trains, what exactly, in which format and how skill retention is checked.

Minimum training package

Short 30–60 minute sessions per role plus on‑the‑job materials are usually enough. Minimum spec items: guide for common tasks (5–10 scenarios), query templates and examples of good phrasing, a checklist “what to do if an answer is doubtful” and escalation paths, an onboarding course for new staff (including a test or control case), and a schedule for refresher training after model updates.

The interface should include prompts and warnings: example queries, alerts before submitting sensitive data, visible limitations (e.g., “this is not legal advice”) and the ability to mark an answer as helpful or incorrect.

Usage rules and responsibility

Define what data may be sent to the AI and what is forbidden. Typically prohibit personal data without justification, trade secrets, internal passwords and restricted documents. Describe how to anonymize text and who verifies it.

Document process changes: where AI gives a recommendation and where humans make the decision. Assign an approver per scenario (shift manager, process owner) and define accountability if an AI recommendation proves wrong.

Provide a feedback channel: where to report errors, disputed answers and improvement ideas, which fields to fill (context, expectation, actual result) and the expected response time from the team.

Example scenario: from pilot to production

Pilot: an AI assistant helps first‑line service desk staff handle employee requests. It suggests a draft reply, finds knowledge base articles and fills ticket fields (category, priority, department). Describe this path as a chain of steps, not just a “chat for support.”

Required data usually includes 6–24 months of ticket history, the knowledge base, response templates and reference tables (services, departments, SLAs). Access is limited by need‑to‑know: the assistant sees only ticket fields required for a reply and personal data (phone, ID, address) is hidden or masked. Specify where data is stored and who can export datasets.

Integrations should be mandatory: ticket system (create/update/close), knowledge base (search and links), email or messenger (notifications and replies). Without these the pilot often becomes a “manual chat” that fails to deliver value.

Acceptance metrics should differ for pilot and production. For a 4–8 week pilot monitor share of suggested answers accepted by agents, reduction in average response time and routing accuracy. For production focus on monthly quality stability, SLA compliance, share of cases without escalation, and security incident control.

Common risks and mitigations in the spec: leaks via logs and prompts (ban storing sensitive data), hallucinations (answers only based on knowledge base with links), quality degradation from new request types (retraining and monitoring plan), vendor lock‑in (export formats, portability requirements).

Acceptance, common mistakes, checklist and next steps

Acceptance ensures the system solves the task, is secure and maintainable. Acceptance criteria in the spec should be written so different teams (business, IT, security) reach the same conclusion.

Group acceptance criteria into four areas: functional (which scenarios are supported, role access, constraints and fault tolerance), quality (target metrics, allowed error rates, rules for handling uncertainty), security and compliance (data handling, access control, logging, personal data requirements) and SLA (response time, availability, support reaction times, change windows and rollback plan).

Prepare test sets in advance: control cases from real work, edge cases (rare phrasing, noisy data, empty fields), and regression tests after each update. For call center helpers include short, emotional and ambiguous requests and checks that the system does not disclose unnecessary information.

Frequent mistakes: metrics described in words (no numbers), missing data owners and quality owners, and no production monitoring plan (what to track, how to react, who’s on duty).

Before finalizing, run a short checklist: goals and scope (what is included and what is not), data (sources, rights, quality, labeling, storage), integrations (which systems, APIs, failure behavior), security (access, audit, compliance), operation (monitoring, updates, SLA, rollback plan).

Next steps after the draft spec: assess infrastructure (where AI will run and required capacity), choose a support model (in‑house or contractor) and schedule a pilot with dates. If on‑prem deployment and integration into the corporate contour is needed, GSE.kz can be useful as a systems integrator and server/workstation provider for AI workloads.