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AI Enablement for Existing Applications: Models Inside Daily Workflows
AI enablement adds model-driven features, such as document extraction, classification, summarisation, assisted drafting or agent access, to applications people already use rather than building a separate AI product. NTSPL prepares the application's APIs and data, routes model calls through a governed AI gateway with redaction and evaluations, and designs human review and fallbacks for teams that want AI inside existing workflows.
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Why AI pilots stall outside the application
Most AI pilots in Indian enterprises live outside the systems where work happens: a chatbot in a separate tab, a notebook demo, a proof of concept that extracts fields from PDFs but never writes them back. The blocker is rarely the model. It is the application: no API to fetch case context, no place in the screen flow for a suggestion, no audit trail of who accepted an AI output, and nothing stopping a model call from sending a customer's PAN to an endpoint outside India. Without that plumbing, accuracy debates never reach production.
NTSPL treats AI as a feature of a modernised application. We pick two or three tasks inside an existing workflow where a model can save measurable time, expose the context they need through internal APIs, and route every model call through an AI gateway that applies redaction, prompt templates, model routing, quotas and logging. Outputs return as suggestions the user accepts or edits, recorded for audit and for evaluation. Each feature ships with an evaluation set, a confidence threshold that falls back to the manual path, and per-feature cost tracking.
Workflows ready for embedded AI features
Signals you need AI Enablement
- Staff spend more than 20% of handling time reading documents or notes to fill fields or decide next steps.
- An AI proof of concept met its accuracy target but has no route into the production application.
- Several teams call model APIs directly with their own keys and no central logging, quotas or redaction.
- You want AI agents or assistants to act on your systems, but the applications lack clean, permissioned APIs.
- Regulators or auditors ask how AI-generated outputs are reviewed and recorded.
When to choose something else
- A standalone knowledge assistant over documents, with retrieval and citations, is Enterprise RAG under Generative AI Solutions.
- Classical prediction on structured data, such as churn, credit risk or demand, belongs to Predictive Analytics & ML.
- If the application has no API layer or is mid-rewrite, do API Enablement first and add AI features afterwards.
AI enablement work packages
Task selection and baseline
Time-and-motion baseline on candidate tasks, with error costs and volumes, ranked by value and risk.
Application readiness
Internal APIs for context retrieval and write-back, UI slots for suggestions and event hooks.
AI gateway
Central service for routing, prompt templates, redaction, quotas, caching, logging and cost attribution.
Model integration
Managed model APIs, document extraction services and, where justified, smaller tuned models.
Evaluation and guardrails
Golden datasets, offline evaluations per release, content filters and confidence thresholds.
Human review and audit
Accept, edit and reject capture, review queues for low-confidence cases, and audit trails.
How AI Enablement Works, Step by Step
Governed AI Gateway Between Applications and Models
The application sends a task request, with context fetched from its own APIs, to an AI gateway that redacts sensitive fields, applies a prompt template and routes to the chosen model. Responses pass guardrails and a confidence check before returning as suggestions, and user decisions flow back into evaluation datasets.
Select any component
Governed AI Gateway Between Applications and Models: click any of its 10 components to see its role and the design choice NTSPL makes for it, or trace the 7-step primary path.
Managed model platforms on each cloud
Bedrock offers several model families under one API, with Guardrails for PII filtering and denied topics. We confirm model availability in Mumbai and use cross-region inference only where data classification allows.
Microsoft Foundry hosts Azure OpenAI and other models with built-in evaluations, while API Management AI gateway policies add token limits, semantic caching and per-application usage metrics.
Gemini Enterprise Agent Platform (formerly Vertex AI) serves Gemini and partner models with evaluation tooling; Document AI extracts fields, Model Armor screens prompts and responses, and Apigee can enforce token quotas.
Design choices for AI inside business applications
Suggest or automate
Start every feature as a suggestion the user accepts or edits. Move to straight-through automation only for cases above a confidence threshold where four to six weeks of data show 97-98% unedited acceptance, and keep sampling 2-5% for human review. Regulated decisions, such as claim rejection, credit or KYC outcomes, stay human-approved.
Model selection and routing
Route by task: small, fast models for classification and extraction, larger models for summarising long or ambiguous inputs. Evaluate two or three candidates on the golden set and pick the cheapest that meets the quality bar. Repeat the comparison quarterly, because model prices and capabilities change faster than most procurement cycles.
Data residency and redaction
Classify every field each feature sends. Redact or tokenise identifiers such as PAN, Aadhaar, account and phone numbers before the call when the model does not need them. Prefer in-country endpoints for personal data where available, and document any cross-region processing against your DPDP Act 2023 notice and sector rules.
Evaluation before every release
Build a golden set of 200-500 real, labelled cases per feature and refresh it monthly from user edits. Any prompt, model or retrieval change must meet or beat the current score on accuracy and safety checks before release, exactly as a failing unit test blocks a build. Publish the scores with each release.
Shipping the first embedded AI feature
Task selection and baseline
Time candidate tasks, cost their errors and pick two or three with clear value.
Application readiness
Build context and write-back APIs and the suggestion slot in the existing screen.
Gateway and first feature
Stand up the gateway with redaction, routing and logging, and build the first feature.
Evaluation and pilot
Score against the golden set, tune thresholds and pilot with 20-50 users.
Rollout and cost tuning
Widen the rollout, route to cheaper models where quality holds, and set budgets.
What you receive
Embedded AI feature targets
| KPI | Typical target |
|---|---|
| Handling time on the targeted task | 25-50% lower than baseline |
| Unedited acceptance of suggestions | Above 85% before wider rollout |
| Golden-set accuracy | Meets agreed threshold with no regressions |
| Model cost per completed task | Within the feature budget, often INR 2-5 or less |
| Identifiers sent to models without need | Zero |
Risks of putting models into production workflows
| Risk | How NTSPL prevents it |
|---|---|
| Wrong or invented outputs entering records | Suggestions require user acceptance, and structured outputs are validated against schemas and source fields. |
| Sensitive data sent to models | Gateway redaction and per-feature data classification; direct model keys are blocked at the network and identity layers. |
| Cost creep from long prompts | Token budgets per feature, prompt caching and routing to smaller models. |
| Model updates degrading quality | Pinned model versions and evaluation gates before any upgrade. |
Embed AI into our own applications or buy an AI copilot product?
Buy when the task lives in a tool that already has a capable copilot, such as email, documents, CRM or ITSM suites, and the vendor's data handling meets your requirements. Embed when the work happens in your own applications, such as claims, loans, orders or dispatch, when outputs must write back into your records with an audit trail, or when regulated data needs your own redaction and residency controls. Embedding costs more upfront but places AI where the time is spent; standalone copilots often see low use because staff must copy context between windows.
Key facts: AI Enablement
AI Enablement in Practice
2 AI Enablement scenarios, from Stock broking (exchange member) and Mobile casual gaming (no real-money games), plus related platform case studies.
Stockbroker cuts account-modification review from 2 days to 3 hours with AI pre-fill
Casual games studio triages 40,000 daily player tickets with Gemini inside its console
Wealth firm's research assistant cuts RM client-prep time from 50 to 12 minutes
Infrastructure company's legal team reviews contracts in hours, not days
AI Enablement: Frequently Asked Questions
What does an embedded AI feature cost to build and run?
A first feature with gateway, evaluation set and pilot typically takes 8-10 weeks and INR 25-45 lakh. Run cost depends on volume and model choice: extraction and classification often cost under a rupee or two per document, while long summaries cost more. We set a per-task budget and route to smaller models to stay inside it.
How do we handle personal data and regulators when using LLMs?
Classify what each feature sends, redact identifiers it does not need, and prefer Indian-region endpoints for personal data. Record consent and purpose under the DPDP Act 2023, keep humans approving regulated decisions, and log AI-assisted actions so RBI, SEBI or IRDAI audits can see who decided what.
Which model should we use?
Whichever passes your evaluation set at the lowest cost and latency. We usually compare two or three models available on your cloud's managed platform and revisit the choice every quarter. The production model version is pinned, and upgrades go through the same release gate as code.
Can AI agents use our applications directly?
Yes, if the application exposes permissioned APIs. We design narrow, audited tools, such as get claim summary or create follow-up task, that agents call through the gateway with the user's identity, and keep write actions behind confirmation steps. Protocols such as the Model Context Protocol make those tools discoverable.
How do we know the feature is working?
Measure against the baseline: handling time, error rates and acceptance of suggestions. We also track how often and why users edit outputs, which feeds the evaluation set. A feature that saves time but shows rising edits is investigated before it is expanded.
Do we need to fine-tune models?
Usually not at first. Prompt templates, good context from your APIs and structured output schemas solve most extraction and drafting tasks. Tuning a smaller model makes sense later, when volumes are high and a cheaper model can match quality on your evaluation set.