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Generative AI

"The Future of Enterprise Productivity Starts Here"

Generative AI is revolutionising how organisations create, manage, and use information.

ArtAgile helps enterprises implement Generative AI solutions that enhance productivity, automate knowledge workflows, and improve decision-making. Our services include AI copilots, enterprise knowledge assistants, intelligent content generation platforms, and advanced data-driven insights systems. We design secure and scalable Generative AI solutions tailored to enterprise environments.

Capabilities

Generation surfaces we build for enterprises.

AI Content Generation
AI Chatbot & VAs
Document Generation
AI Code Generation
Video, Image & Audio
Enterprise Gen AI

Outcomes

What next-gen AI power delivers in production.

AI copilots for enterprise teams
Knowledge assistant platforms
Intelligent content automation
Advanced data-driven insights
Secure & compliant deployment
Multi-modal AI capabilities

Why ArtAgile?

ArtAgile delivers enterprise-ready generative AI systems that are secure, scalable, and aligned with business needs. Our team focuses on practical implementations that enhance productivity while maintaining strong data protection standards.

What we build

Five capability surfaces — one integrated platform

Each surface solves a different enterprise problem. Most engagements combine two or three into a unified solution layer.

RAG & Knowledge Assistants

Retrieval-augmented generation connects a language model to your proprietary documents, databases, and APIs — so answers are grounded in your data, not general internet training. We build hybrid retrieval pipelines (dense vector + BM25 keyword) with re-ranking to push accuracy high on domain queries before any fine-tuning is needed.

Chunking and embedding strategy tuned per document type
Vector store selection (pgvector, Qdrant, Weaviate, Pinecone)
Metadata filtering and access-control aware retrieval
Context-window management for multi-turn conversations

AI Copilots

Copilots are LLM-powered assistants embedded inside the tools your teams already use — Slack, Teams, Salesforce, ServiceNow, or custom web apps. They handle complex multi-step tasks by calling internal APIs and surfacing enterprise data, while keeping a human in control for irreversible actions. We use function-calling and tool-use primitives to connect copilots to live systems without risky open-ended execution.

Intent classification and slot-filling for structured workflows
Tool-use schemas for CRM, ITSM, ERP, and data APIs
Streaming response with responsive low-latency targets
Role-based persona and knowledge scope controls

Document Generation

Structured document generation uses prompt templates, variable injection, and post-processing formatters to produce contracts, RFP responses, compliance reports, and technical specifications at scale. We pair generation with a human-review workflow so output is auditable and edit-traceable — meeting compliance requirements in regulated sectors.

Template library with version-controlled prompt chains
Output format targets: DOCX, PDF, Markdown, structured JSON
Review-and-approve workflow with diff-tracking
Brand voice guardrails applied at generation time

AI Code Generation

Code generation accelerates engineering teams through context-aware autocompletion, unit-test synthesis, legacy code explanation, and automated refactoring — integrated into CI/CD rather than just IDE plugins. We instrument AI-assisted PRs with quality gates (static analysis, coverage delta, security scan) so generation speed does not introduce regressions.

Repository-aware context via local code embeddings
Language support: Python, TypeScript, Java, Go, SQL, IaC
PR-level test generation targeting strong line coverage
Technical-debt tagging with estimated remediation effort

Multimodal AI

Multimodal pipelines process images, PDFs, audio transcripts, and video frames alongside text — enabling use cases like automated invoice processing, visual quality inspection, meeting intelligence, and product catalogue enrichment. We select vision models, OCR layers, and audio-to-text components by task, then stitch them into a unified data pipeline with structured output contracts.

Document vision: layout-aware extraction from scanned PDFs
Image classification and object detection for operations
Meeting and call transcript analysis with speaker diarisation
Structured output schemas for downstream system ingestion

Enterprise Gen AI Platform

For organisations deploying multiple Gen AI use cases, we build a shared platform layer — a single API gateway, prompt registry, model router, token usage dashboard, and audit log — so each new use case reuses governance infrastructure instead of re-inventing it. The platform supports model substitution (swap GPT-4o for Claude or a private model) without application-layer changes.

Central prompt registry with versioning and A/B testing
Model router for efficiency, latency, and compliance routing
Token usage dashboard and per-team usage controls
Unified audit trail for all LLM interactions

Where it lands

Use cases by business function

Generative AI delivers real productivity gains across every enterprise function. These are the most common starting points.

Customer Support

Resolve faster, escalate smarter

AI tier-1 agent handling the bulk of routine queries
Real-time agent assist with next-best-response suggestions
Automatic ticket summarisation and routing
Knowledge base gap detection from unanswered queries
Sentiment-triggered escalation to human agents

Sales & Revenue

Win more, prepare faster

RFP and proposal first-draft generation from templates
Personalised outreach copy from CRM context
Deal-summary and next-step recommendations post-call
Competitive battlecard synthesis from market data
Forecast narrative generation for QBR decks

Operations

Eliminate manual knowledge work

Invoice and purchase-order extraction and validation
SOP-to-checklist conversion and update automation
Contract clause analysis and obligation extraction
Regulatory change-impact summarisation
Incident report drafting from log data

Engineering

Ship with fewer review cycles

Code review pre-flight: security, style, and logic checks
Legacy codebase Q&A and onboarding assistant
Test case generation from acceptance criteria
API and internal docs generation from code
Root cause analysis from error logs and traces

Enterprise-grade

How we make it production-safe

Turning a language model into a dependable production system takes engineering discipline. We apply a six-layer quality and safety stack to every engagement so that what ships is reliable, auditable, and efficient at scale.

Evaluation Framework

Automated LLM-as-judge and human evaluation runs on every prompt change. We track faithfulness, relevance, groundedness, and task-specific metrics before any version reaches production.

Benchmark dataset curated from real user queries
Regression test suite in CI pipeline
Golden-set comparison on every model upgrade

Guardrails & Safety Filters

Input and output classifiers intercept prompt injection attempts, off-topic queries, and policy-violating responses before they reach end users or downstream systems.

PII detection and redaction at input layer
Topic and tone classifiers on output
Jailbreak and prompt-injection pattern detection

Observability & Tracing

Every LLM call is logged with prompt, response, latency, token count, and retrieved context references. Distributed tracing connects AI calls to application spans for root-cause analysis.

LLM span instrumentation (OpenTelemetry compatible)
Token usage attribution per user, team, and feature
Anomaly alerts on latency or quality regressions

Data Privacy & Isolation

Your proprietary data never trains a shared model. We deploy private vector stores, enforce tenant-level data isolation, and support VPC-deployed or on-premises inference where data residency rules require it.

No training on customer data in hosted API calls
Tenant-scoped retrieval with row-level access control
Private deployment on Azure, AWS, or GCP (bring your own key)

Grounded Accuracy

We keep answers factual through retrieval grounding, citation enforcement, confidence thresholds, and structured output schemas that guide the model to populate clearly defined fields.

Source citation required on every factual claim
Structured JSON output that keeps responses grounded and on-spec
Low-confidence routing to human review queue

Efficiency & Latency Governance

Token usage and response times are actively managed for efficiency. Prompt compression, caching, and model-tier routing keep performance predictable and consistent at scale.

Semantic caching for repeated query patterns
Prompt compression that meaningfully trims token consumption
Dynamic model routing (GPT-4o vs GPT-4o-mini by task complexity)

What you get

Deliverables at every stage

We treat each deliverable as a working artefact your team can operate and extend. Every engagement ends with production-running software and documentation you can build on from day one.

Gen AI Strategy & Use-Case Roadmap Prioritised use-case backlog with effort, impact, and data-readiness scored for each item.
Proof-of-Concept Application Functional end-to-end prototype with target use case, demo dataset, and benchmark report — delivered quickly.
Production-Grade RAG or Copilot System Deployed application with retrieval pipeline, prompt library, guardrails stack, and observability instrumentation.
Evaluation & Benchmark Suite Curated test dataset, evaluation scoring pipeline, and baseline metrics you can run against any future model upgrade.
Observability Dashboard Grafana or preferred tooling dashboard covering latency, token usage, quality scores, and user adoption by feature.
Runbook & Handoff Documentation Architecture decision records, prompt library documentation, model upgrade procedure, and a 30-day post-launch support window.

Typical engagement model

Discovery & Data Audit (Week 1–2) Map your data sources, identify candidate use cases, assess data quality and access controls.
Proof of Concept (Week 3–6) Build one targeted PoC with evaluation benchmarks. You see real accuracy and latency numbers before committing to full build.
Production Build (Week 7–16) Harden the PoC into a production system with security, observability, and CI/CD integration. Guardrails and efficiency controls land here.
Launch & Measure (Week 17–20) Staged rollout, A/B baseline comparison, adoption tracking, and handoff to your team with full runbook.
Ongoing Optimisation (Post-launch) Monthly model and prompt review cycles, quality regression monitoring, and use-case expansion as volume grows.

Timelines are typical for a single-use-case deployment. Multi-use-case platform engagements are scoped individually. We will share a detailed estimate after the discovery session.

Common questions

Frequently asked questions

Questions we hear at every initial conversation — answered directly.

How do you keep AI answers accurate and grounded?

Accuracy is achieved through a combination of techniques working together. In RAG systems we ground every answer in retrieved source passages and require the model to cite its sources — answers are backed by retrievable evidence or routed to a human for confirmation. We also use structured output schemas that keep the model focused on populating defined fields, apply confidence thresholds so low-certainty responses are flagged for review, and run automated faithfulness evaluations on every prompt change before it reaches production. Because every LLM has inherent limits, we design workflows that reliably catch and contain edge cases, keeping a human in the loop wherever it adds confidence.

Will our proprietary data be used to train public AI models?

No. When using commercial API providers (OpenAI, Anthropic, Azure OpenAI, Google Vertex), we configure enterprise agreements where API traffic is not used for model training. Your documents and query data stay within your account and are not shared across customers. For organisations with stricter data residency or sovereignty requirements, we deploy models within your own cloud environment (Azure, AWS, GCP) using private endpoints and VPC peering, or on private inference infrastructure — so data never leaves your network boundary. Your vector store and any fine-tuned model weights are also maintained under your ownership.

Which AI models do you use — and can we switch later?

We are model-agnostic by design. We select from the current leading options — GPT-4o, Claude 3.5/3.7, Gemini 1.5 Pro, Llama 3, Mistral, and domain-specific fine-tuned variants — based on your task type, latency needs, data privacy requirements, and efficiency targets. Switching models later is straightforward because we build an abstraction layer (model router and prompt registry) that decouples your application from any specific provider. A model change typically requires re-running the evaluation suite and adjusting prompt templates — it does not require rewriting application logic. We include model substitution procedures in the handoff runbook.

How do you keep a Generative AI project efficient to run in production?

Runtime efficiency depends on query volume, context window size, model tier, and how effectively you apply caching. A well-tuned customer-support copilot on a lighter model with semantic caching runs very efficiently, while a more complex RAG system on a larger model demands more resources. We build usage dashboards from day one so you see actual consumption per feature and per user. Prompt compression and model-tier routing (sending simpler queries to lighter models) meaningfully improve efficiency while preserving quality; we apply both as standard practice.

Do we need to fine-tune a model, or is RAG enough for most enterprise use cases?

For most enterprise knowledge and productivity use cases — internal search, customer support, document Q&A, copilots — a well-built RAG system outperforms a fine-tuned model because it can be updated instantly when your data changes, without retraining. Fine-tuning is valuable when you need the model to adopt a very specific output style or vocabulary, or when latency requires a smaller model to perform tasks a larger base model handles poorly. We recommend starting with RAG, measuring accuracy on your evaluation dataset, and only pursuing fine-tuning if there is a documented gap that RAG cannot close. Fine-tuning also carries higher operational overhead (dataset curation, training runs, re-evaluation) that is only warranted when the accuracy delta justifies it.

Pick a sub-service to see capabilities, approach, and deliverables in depth.

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Talk to us about Generative AI

Tell us about your data, your systems, and the outcome that matters most. We will reply with a scoped path forward — usually inside one business day.

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