Service
Enterprise AI
AI that runs inside your business, under your control.
Executive summary
What this practice is for.
Most organisations are past the experimentation phase and stuck at the deployment phase. Pilots prove that a model can answer a question; they rarely prove that a business process is faster, cheaper or safer as a result.
SJAIN Tech builds enterprise AI the way we build any critical system: with an owner, an architecture, a data contract, an evaluation harness and a support model. We start from the process you want to change, then choose the smallest model footprint that changes it.
The result is AI that survives audit, integrates with the systems you already run, and produces a number your CFO recognises.
Business challenges
What we are usually called in to fix.
Pilots that never reach production
Promising prototypes stall because nobody owns integration, evaluation or run cost.
Data that models cannot trust
Unstructured content is scattered across drives, mailboxes and legacy systems with no lineage.
Governance gaps
No policy for what a model may see, say or decide — so risk teams block rollout.
Unbounded run cost
Token spend grows faster than value because no one measured cost per resolved task.
Why traditional approaches fail
- Tool-first buying: a licence is purchased before the process is understood, so adoption never happens.
- Generic chatbots answer generically because retrieval was never grounded in your authoritative sources.
- Accuracy is assessed by demo, not by a repeatable evaluation set with human review.
- Security is retrofitted, forcing a rebuild when the model touches regulated data.
Methodology
The SJAIN method.
Six stages, each with defined inputs, outputs and owners.
- Step 01
Discover
Map the processes, decisions and documents where AI can move a real number.
- Step 02
Assess
Score data readiness, risk exposure, integration effort and expected ROI per use case.
- Step 03
Architect
Choose model strategy — hosted, private or hybrid — plus retrieval, guardrails and observability.
- Step 04
Engineer
Build pipelines, agents and interfaces with an evaluation harness from day one.
- Step 05
Integrate
Embed into ERP, CRM, service desk and workflow tools so users never change context.
- Step 06
Operate
Monitor accuracy, cost, drift and adoption; retrain and tune on a fixed cadence.
Scope of services
What is included
- AI opportunity mapping and value case
- Model selection and private hosting strategy
- Retrieval-augmented generation over enterprise content
- Agentic workflows with human-in-the-loop approval
- Evaluation harness, red-teaming and accuracy benchmarks
- Integration with core business systems
- Governance framework, policy and audit logging
- Enablement, change management and adoption tracking
Deliverables
What you receive
- AI readiness and opportunity assessment
- Reference architecture and security model
- Production AI services with CI/CD
- Evaluation dataset and scoring dashboard
- Governance policy pack and audit trail
- Run-book, SLA and support model
Architecture overview
How it is built.
- 01
Ingestion layer normalising documents, tickets, records and telemetry into a governed corpus.
- 02
Embedding and vector index with tenant, role and document-level access control.
- 03
Orchestration layer routing between retrieval, tools, business APIs and models by task and cost.
- 04
Guardrail layer enforcing policy, PII handling, refusal rules and approval gates.
- 05
Observability layer capturing prompts, responses, citations, latency, cost and human feedback.
Implementation process
Weeks 1–2
Discovery workshops, use-case scoring and value case.
Weeks 3–6
Architecture, data pipeline and first working slice in a controlled environment.
Weeks 7–12
Evaluation, hardening, integration and pilot rollout to a real user group.
Quarter 2 onward
Scale to further use cases on the same platform, with cost and accuracy under management.
Security & compliance
- Private or VPC-hosted models where data residency demands it
- Role-based retrieval so the model can only see what the user may see
- PII detection, redaction and retention policy
- Full prompt and response audit logging for regulator review
- Human approval gates on any consequential action
Benefits & ROI
The numbers this practice moves.
0–70%
Manual effort removed from document-heavy work
0x
Faster case and file review cycles
0–6 mo
Typical payback on a scoped first use case
Capabilities in this practice
Industries served
Related technologies
Related AI solutions
Frequently asked questions
Rarely at the start. Most value comes from retrieval, orchestration and integration. Private hosting matters when data residency, IP or regulator posture demands it — we design so you can move later without a rebuild.
Every engagement ships an evaluation set drawn from your real cases, scored automatically and reviewed by your subject-matter experts before release.
We model cost per resolved task, not tokens in isolation, and route cheaper models to easier work so unit economics improve as volume grows.
Related case study
Document automation for health claims
70% of manual claims effort removed
Read the case studyDownloadable guide
Enterprise AI capability guide
A practical guide covering methodology, architecture patterns, deliverables and typical commercial models.
Not sure where this practice
fits your roadmap?
We start with a discovery workshop: current state, constraints, the highest-value first slice and what it costs to prove it.