Services/AI Solutions
Enterprise AI · Production-Ready · Governance-First

AI that works
in production.
Not just in the demo.

We build and deploy AI solutions for enterprise teams - from knowledge assistants and document intelligence to voice automation and discovery pilots. Every solution is architected for governance, auditability, and the constraints of regulated environments.

Discuss a use caseSee how we govern AI

What separates production AI from prototype AI

AI that ships.
Not AI that slides.

Most enterprise AI initiatives fail at the same three points: they cannot answer for the model's decisions, they break under production load, or they were never integrated with the systems that matter. We build AI differently - starting with the governance model, not the demo.

01 - Governance first

Every AI decision needs to be explainable to someone

In a regulated environment, a model that gives the right answer 95% of the time without an audit trail is a liability, not an asset. We architect explainability, access controls, and audit logging before a single model is deployed.

02 - Integration depth

AI is only as useful as the systems it can see and touch

A knowledge assistant that cannot access your actual knowledge base, or an extraction tool that does not connect to your operating systems, is a common and expensive failure. We design the integration architecture first.

03 - Production standards

The model that ran in your sandbox will not survive your traffic

Latency at scale, context window management, hallucination mitigation, fallback paths, and failover are engineering problems. We solve them before launch, not after the first production incident.

AI Solutions Services

Five solutions.
One engineering standard.

Each solution is built for enterprise environments - integrated with your existing systems, governed by design, and owned by your team from the day it goes live.

01 - Enterprise Knowledge Assistant

Secure assistants that give your teams instant answers from your actual knowledge base

Most enterprise employees spend meaningful time searching for information that already exists somewhere in the organisation. Knowledge assistants built on RAG architectures change this, but only when they are connected to the right sources, governed correctly, and integrated into the workflows people actually use.

We design, build, and deploy knowledge assistants grounded in your internal documentation. Role-based access controls ensure users only see what they are authorised to see. Every response is sourced and traceable.

Industry benchmarks

40%

Reduction in support tickets - McKinsey, 2024

3.2h

Weekly time recovered per employee - Gartner, 2024

Architecture

RAG pipelineVector storeClaude / OpenAIRBACAudit trailSSO integrationSlack / Teams
AI

Vendor contracts above $50k require dual approval from Finance and Legal.

Source: Procurement Policy v4.2 · Page 12

JD

What is the approval threshold for new vendor contracts?

AI

Document_Intake_047.pdf

Unstructured enterprise document · 4 pages

Extracted

Entity ID

BCB-447821-X

Classification

J45.20 / HME

Authorising Party

Dr. M. Okafor

Reference Date

22 Jan 2025

Service Codes

99213, 94060

Route

ERP Integration

Extraction confidence99.8%

02 - Enterprise Document Intelligence

Structured data from unstructured documents - automatically, accurately, at volume

Every enterprise has a document problem. Referral forms, CMNs, contracts, compliance submissions, financial instruments, claims, and procurement records contain the data that drives revenue, risk, and operations.

We build confidence-gated document intelligence pipelines that extract structured fields from unstructured documents at scale and deliver validated output into ERP, RCM, case management, or compliance systems.

From VARP production engagement

99.8%

Field-level extraction accuracy in production

47%

Reduction in document-to-ERP turnaround

Architecture

Document AIOCR pipelineConfidence gatingEHR / ERP integrationICD-10 / HCPCS mappingHIPAA · GDPR compliantField-level audit trail

03 - Workforce Intelligence Assistant

AI that reduces HR overhead, surfaces attrition risk early, and keeps every decision auditable

Every enterprise workforce generates intelligence that never gets acted on. Leave patterns, performance trajectories, engagement signals, policy query volumes - the data exists, but it lives across disconnected systems.

We build workforce intelligence assistants that handle routine HR queries while monitoring multi-signal risk indicators across the workforce through a governed conversational interface.

From VARP production engagement

40-50%

Reduction in HR operational workload

60-90days

Earlier attrition risk detection

Architecture

Policy RAGMulti-signal intelligenceSentiment analysisHRIS / payroll integrationEscalation logicExplainability layerFull audit trail

Risk signal monitor

Engagement score drop - Dept. Sales West

-18 pts over 6 weeks · 12 employees affected

High

Policy acknowledgement overdue

Q4 Code of Conduct · 34 employees pending

Medium

Unusual PTO pattern detected

Engineering team · Above 2σ baseline

Medium

Onboarding completion rate

Jan cohort · 94% on track

Normal

Detected intent

Check order status - #ORD-44821

Response

Your order is in transit. Estimated delivery: Thursday before 6 PM.

Voice IVR

Web Chat

WhatsApp

Teams Bot

04 - Voice & Chat AI Assistants

High-volume request resolution without sacrificing quality, control, or escalation

Voice and chat automation deployed poorly creates the worst of both worlds: low deflection rates and frustrated users trapped in loops. The failure is usually in intent classification, escalation design, or the assistant knowledge layer.

We design and integrate assistants with clear intent taxonomy, graceful escalation paths, and instrumentation to measure what is actually resolved across IVR, web chat, WhatsApp, and internal collaboration tools.

Industry benchmarks

50%

Reduction in human-handled volume - Gartner, 2024

24/7

Coverage without headcount increase

Architecture

NLU / intent classificationIVR integrationWhatsApp APITeams / Slack botsEscalation flowsAnalytics dashboard

05 - AI Discovery & Pilot Delivery

Structured programs to prove ROI and de-risk scale-up before you commit the budget

The question CTOs ask is not "can AI do this?" It is "can AI do this in our environment, with our data quality, under our governance constraints, at a cost that justifies the business case?"

We run structured discovery and pilot programs with measurable success criteria, real data validation, and a go/no-go recommendation with evidence. If the use case holds up, we can take it to production without switching partners.

Typical pilot outcomes

6-8wk

Pilot to go/no-go decision

3.5x

ROI on pilots progressed to production - Deloitte, 2024

Pilot deliverables

Use case validationData readiness auditTechnical feasibilityROI modelGovernance blueprintScale-up roadmap
1

Use case discovery & prioritisation

Business case, data audit, feasibility scoring

Wk 1-2
2

Technical architecture & data preparation

Integration design, pipeline setup, baseline metrics

Wk 2-3
3

Pilot build & controlled deployment

Narrow production environment, monitored rollout

Wk 3-6
4

Results analysis & scale-up recommendation

Evidence report, go/no-go, production roadmap

Wk 6-8

Governance Architecture

The part most AI vendors
skip past in the demo.

72% of enterprise AI projects fail due to governance gaps, not model performance - Gartner, 2024. Governance is not a compliance checklist we add before handover. It is the architectural layer we design before the first model is selected.

Principle 01 - Explainability

Every AI decision needs to be traceable to a source

We build retrieval pipelines that make the source of every response auditable, regardless of the model used. We do not ask the model to explain itself.

Principle 02 - Access control

AI systems that can access everything will eventually expose everything

Every deployment includes role-based access control at the retrieval layer. Users only receive answers from data within their authorised scope.

Principle 03 - Observability

Production AI degrades silently without instrumentation

Response quality drifts, retrieval relevance drops, and confidence thresholds shift as data changes. We instrument deployments to detect degradation early.

Governance Architecture

Five layers. Built before
the first prompt is run.

This is the architecture we deploy across every AI solution - adapted to your industry, integrated with your existing IAM and compliance infrastructure, and documented for independent security review.

Layer 01

Application Layer

Input sanitisation, prompt hardening, user identity context injection, query intent classification, rate limiting, and abuse detection before the model is invoked.

Input sanitisationPrompt hardeningIdentity contextRate limiting

Layer 02

Retrieval & Grounding Layer

Citation-constrained answer generation with traceable source references, retrieval-enforced hallucination bounds, and confidence scoring on every retrieval decision.

Citation enforcementHallucination boundsSource attributionConfidence scoring

Layer 03

Access Control Layer

Role-based access control enforced at the retrieval layer so users only receive answers from data within their authorised scope.

RBAC at retrieval layerData segregationIAM syncSSO integration

Layer 04

Observability Layer

Real-time dashboards for response quality, search success rate, retrieval relevance, confidence distribution, drift detection, and alerting.

Drift detectionQuality scoringAlert thresholdsPerformance dashboards

Layer 05

Audit & Compliance Layer

Complete event log with timestamp, user identity, query input, model output, and source references, formatted for regulatory review.

Immutable event logUser traceRegulatory exportHIPAA · GDPR · SOC 2
HIPAA-ready
GDPR-compatible
SOC 2 alignment
ISO 27001 controls
GxP-aware engineering

Industry evidence

The business case for enterprise AI
is no longer speculative

These figures come from independent research organisations. We use them as directional evidence and replace them with your own production data the moment we have it.

40%

Reduction in internal support ticket volume from AI knowledge assistants

McKinsey Global AI Survey, 2024

3.5x

ROI from enterprise AI pilots that progress to full production deployment

Deloitte AI Institute, 2024

72%

Of enterprise AI initiatives fail due to governance and integration gaps - not model capability

Gartner AI Hype Cycle, 2024

50%

Of high-volume customer queries can be reliably handled by well-designed AI assistants

Gartner Customer Service Report, 2024

All figures from named third-party research. We use these as indicative benchmarks. VARP does not claim these as proprietary outcomes unless explicitly attributed to a client engagement. Actual results vary by use case, data quality, and implementation scope.

How AI Discovery works

Prove the business case
before you commit the budget

A well-run AI pilot answers the questions that a proof of concept never does: does it work with your actual data, in your actual environment, under your actual governance constraints? We run structured six-to-eight week pilots that produce a decision, not a slide deck.

1

Use case discovery & prioritisation

We map candidate AI use cases against business value, data readiness, and technical feasibility, then score them to identify the use case most likely to produce a defensible business case.

Weeks 1-2

2

Data readiness & architecture design

We audit your data sources, integration surfaces, and governance requirements, then design the minimal viable architecture needed to test the use case under real conditions.

Weeks 2-3

3

Controlled pilot deployment

We build and deploy into a narrow production environment with real users, real data, real load, baseline metrics, and continuous measurement against agreed success criteria.

Weeks 3-6

4

Evidence report & scale-up decision

You receive real performance data, a go/no-go recommendation with reasoning, and, if the signal is positive, a production roadmap with scope, team, and timelines.

Weeks 6-8

What you receive at the end of a pilot

A decision - not a presentation

Most pilots end with a polished slide deck and a recommendation to continue exploring. Ours end with a documented evidence report, a clear go/no-go, and a production roadmap ready to execute.

Validated performance data against agreed success criteria
Data quality and readiness assessment with gaps identified
Governance and compliance gap analysis
ROI model with production-scale projections
Go/no-go recommendation with documented rationale
Production roadmap with scope, team, and timeline if positive

AI technology stack

Model-agnostic.
Integration-obsessed.

We work with all major foundation model providers and deploy them using the same engineering discipline we apply to every other production system. The model is chosen for your use case - not for our vendor relationships.

Foundation models

*Claude (Anthropic)OOpenAI GPT-4oGGeminiHFHugging Face

RAG & vector infrastructure

PGpgvectorPIPineconeWVWeaviateLCLangChainLILlamaIndex

Document intelligence

DAGoogle Document AIFRAzure Form RecognizerTXAWS TextractTETesseract OCR

Voice & conversational AI

DFDialogflowBFAzure Bot FrameworkWWhisper (OpenAI)ELElevenLabs

Cloud infrastructure

AWSAmazon Web ServicesAzMicrosoft AzureGGoogle CloudTfTerraform
Start with a structured conversation

Have a use case.
Not sure if it'll survive production?

That's exactly the conversation we're built for. Bring us the use case - we'll tell you honestly whether the data supports it, what the governance requirements look like, and what a pilot would need to show.

Discuss a use caseStart with an AI pilot

No slide deck. Talk to a Solution Architect.