Specific solution · AI + Machine Learning
Generative AI implementation
Put generative AI to work where the work already happens.
EINO connects commercial or open models to trusted data, business systems, review steps, and operating controls. The result is generative AI that people can use in everyday work.
Evidence framework
Define proof before production.
Baselines and targets are agreed for each engagement. These dimensions shape what the team evaluates; they are not promised historical results.
Useful
Does it improve the task?
Test it with the people who will use it and the work it must help them complete.
- Task completion
- Adoption
- Time to answer
- Review effort
Trustworthy
Can people trust the answer?
Check that answers use approved sources, follow the rules, and show when human review is needed.
- Groundedness
- Source coverage
- Policy adherence
- Escalation quality
Operable
Can the business run it?
Track speed, cost, failures, and support needs once the system is in use.
- Latency
- Cost per task
- Exceptions
- Service reliability
Solution portfolio
Four ways to put generative AI to work.
Each solution combines models, business data, system integration, human review, and clear measures of success.
Document Intelligence
Find, summarize, and draft from trusted documents.Use generative AI to search, read, compare, and create documents while keeping sources, permissions, and review steps clear.
Technical capability
- Intelligent Document Processing (IDP)
- Retrieval-Augmented Generation (RAG)
- Document classification + extraction
- Document summarization + Q&A
- Citations + access control
Business application
- Contract analysis
- Report generation
- Policy + regulatory research
- Case summarization
- Enterprise search
Published results + market benchmarks
External research and case-study benchmarks. Results vary by use case.
less document review time
Financial services legal team case studyannual outside-counsel savings
Reported by the same legal teampotential annual time saved
Survey of 2,200 legal, tax, risk, and compliance professionalsto assess a 100-page brief
Compared with two to three hours in the published caseGenerative AI for Software Development
Help developers understand, write, test, and document code.Add AI coding tools to the development workflow while keeping code review, testing, security, and developer ownership in place.
Technical capability
- Code generation
- Code review + refactoring
- Test generation
- Code documentation
- Repository-aware assistance
Business application
- Legacy code modernization
- API development
- Automated testing
- Infrastructure as Code
- Developer onboarding
Published results + market benchmarks
External research and case-study benchmarks. Results vary by use case.
faster coding task completion
Controlled GitHub Copilot researchhigher pull-request merge rate
Enterprise study with Accenture developersincrease in successful builds
Enterprise study with Accenture developersproductivity value vs. annual engineering spend
Estimated economic potentialCustomer Service Automation
Answer common questions faster and give agents better support.Use AI assistants, chatbots, and agent copilots to find answers, prepare responses, route requests, and pass complex cases to people.
Technical capability
- AI chatbots + virtual assistants
- Agent assist + copilots
- Intent detection + routing
- Knowledge base integration
- Human handoff + escalation
Business application
- Customer support
- IT service desk
- Employee support
- Citizen services
- Case management
Published results + market benchmarks
External research and case-study benchmarks. Results vary by use case.
customer-care cost reduction potential
AI-enabled banking customer carelower average handling time
Published US credit-union transformationlower operating expenses
Customer assistance and collections use caseshigher customer satisfaction
Customer assistance and collections use casesContent Generation + Marketing Automation
Create more content while keeping the brand and approval process clear.Create, adapt, translate, and personalize content from approved inputs, with people reviewing work before it is published.
Technical capability
- Content generation
- Brand voice controls
- Personalization
- Translation + localization
- Review + approval workflows
Business application
- Campaign copy
- Product descriptions
- Email + social content
- Training materials
- Technical documentation
Published results + market benchmarks
External research and case-study benchmarks. Results vary by use case.
productivity value vs. marketing spend
Estimated economic potentialestimated annual global value
Marketing productivity potentiallower campaign deployment cost
Published telecom personalization casehigher response rate
Published telecom personalization caseView research sources (7)
These are external benchmarks, estimates, and published case-study results—not guaranteed EINO outcomes. Results depend on scope, system conditions, implementation, and operating context.
- 01Read source
How a financial services legal team cut review time by 75%
Document review time and outside-counsel savings reported by one financial services legal team.
- 02Read source
Future of Professionals Report
Survey of more than 2,200 legal, tax, risk, and compliance professionals.
- 03Read source
Quantifying GitHub Copilot’s impact in the enterprise
Controlled task research and an enterprise adoption study with Accenture developers.
- 04Read source
The economic potential of generative AI
Estimates generative AI productivity value across business functions, including software engineering.
- 05Read source
The AI-powered bank: Rewiring for excellence in customer care
Banking customer-care cost potential and a US credit-union transformation example.
- 06Read source
The promise of generative AI for credit customer assistance
Potential operating-cost and customer-satisfaction effects in assistance and collections.
- 07Read source
How generative AI can boost consumer marketing
Marketing productivity estimates and published personalization examples.
Operating contexts
The constraints shape the system.
Information sensitivity, decision authority, service expectations, and review obligations change what a responsible implementation requires.
Government + public sector
Support document-heavy services while keeping access, sources, review, and human authority visible.
- Casework
- Knowledge access
- Service response
Healthcare + life sciences
Assist administrative and research workflows around sensitive information without displacing accountable decisions.
- Documentation
- Research support
- Administrative work
Financial services
Connect research, policy, and service content to traceable data, model oversight, and reviewable outputs.
- Research
- Policy operations
- Service support
Enterprise architecture
A production system is more than a model.
Useful generation depends on the paths around the model: how context is assembled, how output is checked, and how accepted work moves into operation.
Approved knowledge + systems
Content, records, APIs, and workflow context enter through governed access paths.
Prepare + retrieve
Ingestion, indexing, permissions, and retrieval bring together the right context for each task.
Orchestrate + generate
Prompts, tools, and a model gateway coordinate the chosen commercial or open model.
Validate + control
Citations, policy checks, evaluations, and guardrails test the response before use.
Review + integrate
People review consequential output and move accepted work into the operating workflow.
Controls that cross the system
- Identity + permissions
- Logging + traceability
- Evaluations + monitoring
- Usage + cost
- Feedback + improvement
Deployment patterns
- Managed model APIs
- Private cloud
- Hybrid
- On-premises
Selected around data, integration, control, performance, and ownership requirements.
Model strategy
Use the simplest approach that makes the workflow reliable.
Custom training is not the default. Testing shows whether the solution needs workflow design, retrieval, fine-tuning, or a specialized model.
Portability by design
A model gateway and explicit evaluation criteria make provider choices visible and reduce unnecessary lock-in.
- 01
Configure
Prompt + workflow design
Start by shaping the task, context, tools, and review path around a capable base model.
- 02
Ground
Retrieval over enterprise knowledge
Add current, permission-aware context when the task depends on organizational information.
- 03
Adapt
Fine-tuning where evidence supports it
Fine-tune a model only when testing shows that retrieval and workflow design are not enough.
- 04
Specialize
Custom model development
Invest in a specialized model when the data, operating need, and measurable advantage justify it.
Delivery path
From a use case to a production system.
Five decisions keep the operating need, technical system, and evidence connected from the first working session through production.
- 01FrameUse-case brief + measures
Where should generation help?
Choose the workflow, users, baseline, constraints, and explicit exclusions.
- 02ShapeArchitecture + evaluation plan
What must connect?
Map knowledge, integrations, model options, controls, and operating ownership.
- 03MakeIntegrated working capability
What is the smallest useful version?
Build a working version with real information and the real business workflow.
- 04ProveEvidence + release decision
Is it useful, safe, and supportable?
Evaluate representative tasks, data, users, failure cases, and operating conditions.
- 05OperateOperating model + backlog
How will the system improve?
Establish monitoring, support, feedback, ownership, and a controlled improvement path.
A practical place to begin
Find the right place to begin.
A Generative AI opportunity assessment turns a broad ambition into a grounded investment decision and a credible first delivery path.
Assessment outputs
- Prioritized use cases + exclusions
- Knowledge + data-readiness findings
- Architecture + model options
- Evaluation + control plan
- Pilot recommendation + delivery path