KNOW
Knowledge discovery
Search policies, contracts, procedures and project records in natural language. Answers include source context so a team can verify before acting.
Semantic search · RAG · citations ENTERPRISE AI SOFTWARESINGAPORE · APAC
Turn trusted data
into decisions.
iSolve is a software platform for enterprise teams building evidence-based search, supervised agents and AI workflows across the systems they already use.
6source classes
3control modes
4operating signals
Built for high-context workKnowledge, workflows and model operations in one measured surface.
QUEUE HEALTH96.2%↑ 8.1% / 24H
✦
Policy Update AgentAwaiting approval · 4 steps
READY01
Supplier Risk WorkflowGrounded · awaiting approval
RUNNINGAbout iSolve AI Tech
A product company for
applied enterprise AI.
ISOLVEAI PRIVATE LIMITED is a Singapore-registered software development and information technology consultancy company.
Under the iSolve AI Tech brand, we turn high-context business work into software that teams can inspect, operate and improve. Our focus is the space between a powerful model and a production workflow: the data connections, retrieval logic, user experience and controls that make the result useful.
Meet the team behind the product ↗LEGAL ENTITYISOLVEAI PRIVATE LIMITED
REGISTRATIONUEN 202439032E
INCORPORATED23 September 2024
REGISTERED OFFICESingapore 188108
Artificial intelligence & business data
AI is becoming a practical operating layer: connecting the information a company already has with the decisions its people make every day.
From information
to operational clarity.
Modern language models are powerful, but a model alone is not a business system. The value appears when the model can retrieve the right context, respect the right permissions, use the right tools and leave a clear record of what happened.
iSolve AI Tech brings those layers together. We design the data connections, retrieval systems, agent workflows and user interfaces that help teams move from a question to a defensible next action.
ContextDocuments, systems and business definitions
ReasoningModels selected for each task and risk level
ActionApprovals, updates and hand-offs in existing tools
How teams apply the platform
Five ways AI improves
the operating day.
Our work is focused on repeatable business moments where better context and faster action create a measurable difference.
INTAKE
Intelligent intake
Classify incoming requests, extract structured fields and route work to the right queue — with confidence thresholds for human review.
Document AI · extraction · routingASSIST
Operational copilots
Give analysts and operators a shared view of the signals, history and available actions behind a decision.
Copilots · context · decision supportACT
Workflow agents
Let supervised agents coordinate multi-step work across APIs and internal tools, while keeping permissions and approvals explicit.
Agents · tools · human-in-the-loopPREDICT
Forecasting & anomaly signals
Combine historical patterns with live operational data to surface risk earlier and help teams prioritize the next investigation.
Prediction · monitoring · alertsThe platform foundation
Designed for data
that cannot be guessed.
Enterprise AI depends on more than a prompt. It depends on dependable data paths, clear model behavior and an operating layer that can be improved over time.
Connect the source of truth
Ingest and index structured and unstructured sources, preserve metadata and keep access rules attached to the data.
Choose the right reasoning path
Route tasks between models, retrieval, tools and deterministic rules based on context, confidence and business policy.
Operate with visibility
Trace prompts, sources, actions and outcomes so product and risk teams can understand what the system is doing.
The iSolve platform
Three product layers for
reliable AI work.
Connect enterprise context, supervised action and model operations without rebuilding the way your teams work. Each layer can start small and mature with the workflow.
Ground every answer
Connect documents, databases, internal tools and business definitions. Keep permissions and citations attached as information moves through the workflow.
6 source classes4 retrieval stages
FILES · DATABASES · APIs · EVENTSMove work forward
Turn repeatable decisions into supervised agents with explicit steps, human approvals, tool calls and safe fallbacks.
3 control modes100% step trace
READ-ONLY · APPROVAL · AUTOMATICOperate with evidence
Evaluate, route and observe model interactions across quality, latency, cost and safety so teams can improve the system over time.
4 operating signals1 routing layer
QUALITY · LATENCY · COST · SAFETYHow we deliver
PRODUCT · ENGINEERING · OPERATIONSFrame the workflow
Identify the decision, the people involved and the signal that will prove value before we choose a model or build an interface.
2–3 wk workflow discovery1 measurable outcome
Start with a review ↗Make context usable
Design the ingestion, retrieval and permission layer that turns scattered business knowledge into dependable working context.
6 source classesRole-aware retrieval
Discuss your data ↗Build the action loop
Coordinate tools, approvals and business rules so a team can move from an answer to a safe next action without losing oversight.
3 control modesStep-level audit trail
Explore a workflow ↗Improve in production
Measure quality, latency, cost and risk with an operating model that your product and engineering teams can own.
4 operating signalsContinuous evaluation
Plan the next release ↗Interactive product proof
Test a governed
workflow.
Use a sample supplier-risk question to see how iSolve retrieves context, checks evidence and proposes a controlled next action.
What this shows
Context retrieval · source grounding · human approval path
iSOLVE / WORKFLOW LAB
SUPPLIER RISK⌁
Run the workflow to see retrieval, reasoning and a grounded action plan.
REFERENCE WORKFLOW / SUPPLIER OPERATIONSRESPONSE —
Platform visibility
See how intelligence moves
through the business.
A single operating view brings together the evidence behind an answer, the policy governing a workflow and the action waiting for a person or system. Product, operations and risk teams can work from the same record instead of reconciling separate dashboards.
- Govern every model path.Choose models by task, quality, latency and policy rather than tying the product to one provider.
- Keep evidence attached.Preserve the sources, retrieval path and business context behind each recommendation.
- Put people at control points.Define where work remains read-only, requires approval or can proceed automatically.
6 source classes3 control modes4 operating signals
Explore the working demo ↗Deployment & operations
Designed to meet
your data where it is.
Start with a focused workflow and choose the operating model that matches your security, residency and scale requirements. We keep the application layer portable so the architecture can evolve with the business.
Discuss a deployment path ↗Solutions by operating context
Built around the
work you know.
We start with the operating reality of a team, then choose the right combination of models, data and software.
OPERATIONS INTELLIGENCE
AI should help a team make the next decision.
Bring forecasting, anomaly signals, supplier risk and workflow queues into one operational view. The strongest systems meet teams inside the tools and language they already use.
4 signal families1 decision surface3 approval states
Talk about your workflow ↗Decisions with a paper trail.
Review documents, surface exceptions and keep every recommendation tied to source evidence.
4 control pointsSource · access · threshold · audit
Less swivel-chair work.
Connect tickets, systems and approvals into flows that keep work moving from intake to resolution.
3 connected hand-offsIntake · approval · resolution
Ship AI with a point of view.
Build the evaluation, observability and routing layer behind differentiated AI features.
4 operating signalsQuality · latency · cost · safety
Useful answers, faster.
Give teams the context to resolve complex customer questions without losing a human tone.
2 evidence pathsCitation · human escalation
06 / How we work
A measured path
from idea to impact.
01
Frame the opportunity
Align on the user, the decision and the measurable signal that matters.
02
Prove the workflow
Test retrieval, reasoning and human controls on a narrow, valuable slice.
03
Engineer the system
Harden integrations, evaluation, security and observability for production.
04
Enable the team
Document the operating model and transfer ownership to the people running it.
Responsible by design
Trust is built
into the system.
We treat access, evidence and fallbacks as product requirements — not a compliance appendix. The operating model is designed to be reviewed by product, engineering and risk teams together.
Access boundariesRole + source scopedConnect only the information each workflow and user is allowed to use.
Evidence by defaultCitation + retrieval traceRetain the source context behind an answer, recommendation or action.
Human-in-the-loop3 control modesRun read-only, require approval or allow bounded automatic action.
Operational visibility4 live signalsTrack quality, latency, usage and exceptions across the system.
SECURITY CONVERSATIONS
We can map your data boundaries, retention requirements and operating responsibilities before a production decision is made.
Request a security review ↗Resources & perspectives
PRACTICAL NOTES FOR AI TEAMSWhy enterprise AI is an integration problem
The model is only one component. The hard work is connecting context, permissions and action into a system people can rely on.
Request the note ↗From RAG prototype to production system
A practical view of ingestion, chunking, retrieval evaluation, access controls and observability.
Request the guide ↗Choosing a first AI workflow
How to find the right balance between value, data readiness, risk and the team’s ability to operate the result.
Start a conversation ↗FAQ
A FEW USEFUL ANSWERSDo you replace our existing models?
No. We design a model-agnostic layer so teams can choose the right model for each task and change providers without rebuilding the workflow.
Can you work with our existing systems?
Yes. We typically start with the systems where the work already lives — knowledge bases, ticketing tools, CRM, ERP and internal APIs.
How do you measure an AI workflow?
We define an evaluation set, track groundedness and task outcomes, then monitor latency, usage and failure modes as the workflow evolves.
What does a first engagement look like?
We begin with a focused discovery and workflow proof, then provide a clear technical plan for production implementation.
A point of view
“The best AI system is not the one that speaks the loudest. It is the one your team can inspect, improve and trust on a Tuesday morning.”— iSolve AI Tech
Start a conversation
Bring us the
hard problem.
Tell us where the work gets stuck. We will map the shortest path from signal to a useful first system, then outline the data, deployment and ownership decisions behind it.
01 Technical review02 Workflow scope03 Deployment path
REQUEST A TECHNICAL REVIEW
hello@isolveaitech.comRegistered office
21 Tan Quee Lan Street #02-04
Heritage Place, Singapore 188108
Coverage
Singapore · Malaysia · Vietnam · APAC