01
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 LIVE SYSTEMAI OPERATIONS CONTROL PLANESINGAPORE / APAC
Make every
signal actionable.
iSolve AI Tech develops enterprise software that connects business knowledge, operational data and supervised AI workflows in one accountable platform.
24/7observable agents
1 layerfor models + data
APACbuilt for scale
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
Software that makes
intelligence operational.
ISOLVEAI PRIVATE LIMITED is a Singapore-registered software development and information technology consultancy company.
Operating under the iSolve AI Tech brand, the company focuses on enterprise AI applications, knowledge systems, workflow automation and the software infrastructure required to operate them responsibly. We work from business requirements through architecture, product engineering, deployment and ongoing improvement.
Discuss a project ↗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.
02
Intelligent intake
Classify incoming requests, extract structured fields and route work to the right queue — with confidence thresholds for human review.
Document AI · extraction · routing03
Operational copilots
Give analysts and operators a shared view of the signals, history and available actions behind a decision.
Copilots · context · decision support04
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-loop05
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.
01 / The control plane
One intelligent layer
for the work behind work.
Bring models, business knowledge and operational systems into one calm, measurable surface. We build the connective tissue that makes AI useful in production.
Knowledge fabric
Ground answers in approved documents, live systems and the context your teams already trust.
RAG · SEARCH · DATAAgent workflows
Turn repeatable decisions into supervised agents with clear steps, permissions and fallbacks.
ORCHESTRATION · TOOLSModel operations
Evaluate, route and observe every model interaction across cost, quality and latency.
EVALS · ROUTING · OPSCapabilities
WHAT WE DELIVERAI product engineering
From first architecture to production release, we turn a high-value workflow into a reliable product surface.
Explore service ↗Data & knowledge systems
Design the pipelines, retrieval layers and permission models that make enterprise context usable.
Explore service ↗Agentic automation
Coordinate tools, approvals and business rules so teams can automate work without losing oversight.
Explore service ↗AI operations
Measure quality, latency, cost and risk with an operating model your engineering team can own.
Explore service ↗02 / Interactive proof
Ask the control plane.
Explore how an enterprise knowledge workflow retrieves context, reasons over evidence and turns an answer into an action.
iSOLVE / WORKFLOW LAB
RUN_001⌁
Run the workflow to see retrieval, reasoning and a grounded action plan.
SUPPLIER OPERATIONS / WORKFLOWLATENCY —
02.5 / Platform telemetry
A calm surface for
serious scale.
MODEL ROUTING / 24H+18.4%
14.8k
Requests routed across approved models
GROUNDED ANSWERSQUALITY
96.2%
Evaluation score · rolling windowACTIVE FLOWSLIVE
38
Supervised automations in production
REFERENCE ARCHITECTUREISOLVE / 4-LAYER
01SignalsCRM · ERP · APIs
→02ContextSearch · RAG · Memory
→03ReasonModels · Agents · Tools
→04ActionTasks · Approvals · Logs
03 / Where it lands
AI that earns
its place in the stack.
01↗
Enterprise search & copilots
Answers with citations, permissions and a clear path to the source.
02↗
Operations automation
Intake, classify, route and resolve the work that slows teams down.
03↗
AI product foundations
Evaluation, observability and model routing for teams shipping AI features.
04 / Built for the real world
Control is
a feature.
Every deployment is designed around the questions enterprise teams ask on day one: where did this answer come from, who can see it, what happens when confidence drops, and how do we improve it next week?
◎ Traceable sources◌ Human approvals⌁ Model-agnostic routing▦ Usage observability
05 / Solutions by 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.
Talk about your workflow ↗Decisions with a paper trail.
Review documents, surface exceptions and keep every recommendation tied to source evidence.
Less swivel-chair work.
Connect tickets, systems and approvals into flows that keep work moving from intake to resolution.
Ship AI with a point of view.
Build the evaluation, observability and routing layer behind differentiated AI features.
Useful answers, faster.
Give teams the context to resolve complex customer questions without losing a human tone.
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.
07 / Responsible by design
Trust is built
into the system.
We treat access, evidence and fallbacks as product requirements — not a compliance appendix.
Access boundariesConnect only the sources each workflow needs.
Evidence by defaultShow the source context behind an answer or decision.
Human-in-the-loopRoute low-confidence or high-impact actions for review.
Operational visibilityTrack quality, latency and usage across the system.
Insights & resources
PERSPECTIVES FROM THE BUILDWhy 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.
Read perspective ↗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
05 / 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.
CONTACT
hello@isolveaitech.comRegistered office
21 Tan Quee Lan Street #02-04
Heritage Place, Singapore 188108
Coverage
Singapore · Malaysia · Vietnam · APAC