At INTOSYN AI Labs, we study how knowledge, models and tools work together to bring AI into real tasks, scientific experiments and decision-making.
We start with testable questions, document methods, evidence and limitations, and advance through reproducible experiments with human judgment at the centre.
Pursue fundamental and interdisciplinary AI research through experiments and research papers.
Let agents use professional tools for retrieval, drawing, drafting and checks.
Coordinate tasks and keep important decisions with qualified people.
Choose cloud compute, private deployment or local hardware for your needs.
Connect knowledge
Use professional tools
Test methods and results
INTOSYN ARCHITECTURE01—04
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RESEARCH & APPLICATIONS
Our research and applications
Four areas connect applied AI, scientific inquiry and agent decision-making. Each starts with a concrete question and investigates it through models, tools and experiments.
Connecting AI with knowledge, tools and real tasks
We study how AI turns specialist knowledge into executable workflows. Engineering design provides a test case for standards retrieval, CAD drafting, parametric modelling and verification, revealing capabilities and limits in real tasks.
Extract goals and constraints from a task, retrieving standards, documents and established methods to ground subsequent actions.
Using AutoCAD and SOLIDWORKS as examples, study how agents create sketches, parametric parts and drawings while recording their actions.
Check dimensions, rules and revisions against task requirements; researchers and domain experts assess the validity of the results.
Research records & outputs
Editable drawings and models / Tool execution records / Validation results
Connect retrieval, analysis and tool use in an inspectable process, comparing effectiveness, reliability and failure conditions across methods.
Scientific judgment & review
Our researchers and domain experts define evaluation criteria, review tool outputs, analyse failures and decide on further improvements.
Read design requirements and parametersRetrieve standards and part templatesGenerate drawings and models with CAD / SOLIDWORKSCheck dimensions, rules and drawing revisionsResearchers and domain experts reviewTask definition / Standards / Parameters and templates / Tool environmentEditable drawings and models / Tool execution records / Validation resultsOur researchers and domain experts define evaluation criteria, review tool outputs, analyse failures and decide on further improvements.
INTOSYN / BIOMEDICINELab concept · Explore inside
Evidence & hypotheses
INTOSYN / BIOMEDICINE LAB
From biological mechanisms to testable hypotheses
At INTOSYN’s biomedicine lab, we investigate biological mechanisms and hypotheses, connecting literature, molecular and omics data, computational analysis and experimental records to develop testable, reproducible methods.
Review mechanisms, targets and related studies; link original sources and data to biological hypotheses for investigation.
Process molecular and omics data, write analysis code and compare parameters and controls, retaining data versions and computational records for inspection and reproduction.
Compare computations with experimental records, review methods, figures and citations, and distinguish evidence from inference when preparing scientific manuscripts.
Research records & outputs
Reproducible analyses / Experimental and method records / Scientific manuscripts
Research questions / Literature and molecular data / Omics data / Experiment records
AI in our research
Our research agents assist with literature searches, data preparation, analysis code and result comparisons, preserving traceable records for hypothesis testing and manuscript preparation.
Scientific judgment & review
Our researchers formulate questions, design controls, assess scientific significance and review conclusions, deciding on further experiments, revised hypotheses and manuscript content.
Our research approach+
We start with open questions in the literature, test hypotheses through data analysis, computation and experimental comparisons, and develop manuscripts that document methods, evidence and limitations. Reproducibility and critical review guide our work.
Define biological hypotheses and evidencePrepare data and an analysis planRun calculations, controls and parameter comparisonsAnalyse results and prepare a manuscriptReview conclusions and next research stepsResearch questions / Literature and molecular data / Omics data / Experiment recordsReproducible analyses / Experimental and method records / Scientific manuscriptsOur researchers formulate questions, design controls, assess scientific significance and review conclusions, deciding on further experiments, revised hypotheses and manuscript content.Review literature for the study, linking mechanisms, candidate targets and data sources while distinguishing evidence, hypotheses and open questions.Check data versions and quality, write analysis code, and define controls, evaluation metrics and computational methods for testing the hypothesis.Run computational experiments, compare alternatives and controls, and document parameters, errors, negative results and ablations for reproducibility.Compare results and prepare figures, methods and references from experimental logs for a scientific manuscript, identifying uncertainty and inferences that need further testing.Our research team reviews methods, evidence and citations, assesses scientific significance and decides on further experiments, manuscript revisions or the next research cycle.
From physical models to reproducible quantum experiments
At INTOSYN’s quantum physics lab, we investigate quantum systems through modelling, simulation and validation. We connect theory, computation and measurement analysis to test physical hypotheses, understand errors and examine the relationship between models and experiments.
Use literature and research questions to formulate Hamiltonians, quantum circuits or open-system models, specifying assumptions, boundary conditions and observables.
Write and run simulations, compare parameters, noise models and control strategies, and retain software versions, random seeds and execution records for reproducibility.
Relate simulations to measurements, examine statistical errors, systematic bias and validity limits, and prepare reviewable analysis for researcher decisions on models and follow-up experiments.
Research records & outputs
Reproducible models and code / Error and measurement analyses / Scientific manuscripts
Research questions / Theory and papers / Models and code / Measurement data
AI in our research
Our research agents assist with literature review, model formulation, simulation code, parameter sweeps and error analysis, connecting tools such as Python, Qiskit and QuTiP with inspectable computational records.
Scientific judgment & review
Our researchers define the physical questions and validation methods, examine model approximations, errors and interpretations, and decide on further computations, measurements and manuscript revisions.
Theory, computation & validation+
Our research starts with a defined physical question and iterates between theory, numerical simulation and measurement analysis. We document approximations, errors and model limits, and develop manuscripts presenting methods and evidence. The space shown is a quantum laboratory concept.
Define the problem and observablesBuild models and simulation codeRun parameter, noise and control studiesCompare measurements, errors and predictionsReview physical interpretations and manuscript conclusionsResearch questions / Theory and papers / Models and code / Measurement dataReproducible models and code / Error and measurement analyses / Scientific manuscriptsOur researchers define the physical questions and validation methods, examine model approximations, errors and interpretations, and decide on further computations, measurements and manuscript revisions.Review related work, define the system, assumptions and observables, and record validity limits and untested premises.Express the problem as a Hamiltonian, circuit or open-system model, generate inspectable code and benchmark against analytical results or known models.Run parameter sweeps, noise and control simulations, compare models with benchmarks, and record convergence conditions, random seeds, software versions and failed runs.Compare predictions with measurements, distinguish statistical errors, model approximations and systematic bias, and retain figures, code and supporting records.Our research team reviews physical interpretations, methods and evidence, prepares manuscripts, defines the limits of the models and identifies further computations and measurements.
We study how agents choose actions with incomplete information, competing goals and constraints. We connect observation, reasoning, tool use and feedback to examine decision grounds, execution traces and failure modes.
Define task goals, environment state and constraints, distinguishing evidence, unknowns and uncertainty that may affect decisions.
Compare candidate actions, plan steps and tool calls, and record decision grounds, state changes and intermediate results during execution.
Use feedback to assess task completion, compare decision strategies and analyse failures. Researchers decide whether to revise, continue or stop.
Research records & outputs
Decision and execution traces / Strategy comparisons / Evaluation and failure analysis
Task goals / Observations / Tool state / Constraints and evaluation criteria
AI in our research
Organise observations and evidence, propose actions, call tools and revise plans from feedback, comparing decision methods on reproducible tasks.
Scientific judgment & review
Our researchers define tasks and boundaries, examine decision grounds and actions, evaluate reliability and controllability, and decide whether actions should continue.
Define goals, observations and constraintsCompare evidence and candidate actionsPlan steps and call toolsEvaluate and revise using feedbackReview the decision: continue or stopTask goals / Observations / Tool state / Constraints and evaluation criteriaDecision and execution traces / Strategy comparisons / Evaluation and failure analysisOur researchers define tasks and boundaries, examine decision grounds and actions, evaluate reliability and controllability, and decide whether actions should continue.Define success criteria, tools and action boundaries, recording initial observations and unknowns so strategies can be compared under the same conditions.Distinguish evidence from assumptions, compare expected effects, costs and risks, and retain the rationale and alternatives not selected.Translate plans into tool calls within task boundaries, recording inputs, outputs, state changes and errors for replay and analysis.Check tool feedback against the goal, identify deviations and unmet conditions, and compare retrying, replanning and requesting review.Researchers review key decisions and complete traces, analyse failures and method limits, and decide to continue, revise or stop, preserving reproducible evaluation records.
We use engineering tasks to investigate agents’ tool use, connecting specialist knowledge, drafting software and execution records to study planning, generation and verification.We integrate research agents into our lab’s work, from literature review and molecular and omics analysis to computational experiments and manuscript preparation. Our researchers continually test the questions, methods and conclusions.We use research agents in our quantum physics work to review literature, formulate models, run simulations and analyse measurements. Our researchers develop and review the physical interpretations, validation methods and manuscript conclusions.We study why an agent chooses an action, how feedback changes its plan, and when it should stop or defer to human review. Complete traces and controlled comparisons help us examine reliable, controllable decisions.
INTOSYN AI LABS / TOOL-USE RESEARCH
From task understanding to tool execution
We use engineering tasks to investigate agents’ tool use, connecting specialist knowledge, drafting software and execution records to study planning, generation and verification.
Editable drawings and models / Tool execution records / Validation results
Engineering research illustration · Task evidence, tool calls and validation records support review by researchers and domain experts.
Scroll through the agent workflow
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Research agents within our labsINTOSYN AI LABS / RESEARCH WORKFLOWSTools for our researchResearch workflow illustration · Methods, code and analysis records remain connected; our researchers review the scientific conclusions.Research records & outputsAgent decisions. From evidence to action.INTOSYN AI LABS / DECISION RESEARCHDecisions & execution feedbackDecision research illustration · Observations, choices and execution traces support evaluation within defined tasks, with human review of key judgments.Research records & outputs
INTOSYN AI LABS / RESEARCH IN PROGRESS
Research in progress
Questions, methods and experimental designs behind our five ongoing projects.
Research infrastructure and application deployment
Computing environments connect experiments with applications. We provide cloud compute, private deployment and local hardware support, configured around data requirements, workloads and existing systems.
Configure compute and software environments for models, computational experiments and application testing.
Private deployment
Connect knowledge, access controls, business systems and research tools within the agreed deployment environment.
Compute cards & local hardware
Plan computing hardware, deployment and maintenance around existing facilities.
COMPUTE YOU CAN HOLD
Compute in the cloud. Or in your own environment.
Run AI in the environment that fits your business. Scroll to unfold the INTOSYN compute card design.
ENCLOSURE STUDY
ON-PREMISES HARDWARE
Bring compute to your own environment.
Compute cards and local deployment for teams that need to manage their computing environment. Configuration and services are planned around existing infrastructure.
Enclosure and dual-core concept visualization. Delivered configuration depends on the agreed solution.
INTOSYN / LOCAL COMPUTE000 / 119
BUILT FOR REVIEW
Traceable work. Human accountability.
Permission-based access
Control access by person, project and document scope.
Source-linked results
Keep files, versions and source locations available.
Recorded activity
Trace retrieval, drafting, edits and approvals.
Professional review
Authorized people own key decisions and final sign-off.
A CLOSER LOOK
Methods and technical details
Explore application examples, interactive experiments and system design.
Use cases & validation+
Case Library
Scenarios and validation records
10 application examples: 6 labelled as solution validation and 4 as demonstrations, presenting methods, conditions of use and review steps.
★ Featured evidence case
C1Solution validated
Feasibility-study first draft
Organise project material, retrieve applicable standards and related cases, and prepare a sourced draft for expert review of data, methods and conclusions.
Review requirementsTechnical routes, evidence approval and sign-off remain with people
C2Solution validated
Standards and mandatory-provision retrieval
Search hundreds of national, industry and local standards plus local supplements in natural language. Results include the applicable provision, source text, title, version, clause and mandatory-status marker.
TechnologyRAG · Version identification · Mandatory-provision tagging
PrincipleKeep conclusions linked to original sources
C3Solution validated
Pre-submission compliance check
Before submission, check the drawing against the relevant standards and produce an issue list: what may not comply, which source and clause supports the finding, and what change may address it.
TechnologyRule execution + human review
Review requirementsThis is a check, not a professional review; the registered engineer remains responsible for sign-off
C4★Solution validated
Domain model training and adaptation
Organise domain corpora, standards and task data; investigate continued pretraining, parameter-efficient tuning and preference optimisation, with task evaluation and version control.
Review requirementsWe do not train a foundation model from scratch for every enterprise
C5★Demonstration environment
Scenario simulation for design changes
A client requests a change in basement floor-to-floor height. Compare clear height, illustrative thresholds, cost estimates and review paths using explicit assumptions; real projects require separate validation.
TechnologyBounded industry world model · State transitions · Scenario simulation
Review requirementsIt does not predict the real world or decide the enterprise’s future
C6Demonstration environment
Intelligent review of material certificates
Extract manufacturer, grade, heat number, chemical composition, mechanical properties, heat treatment and NDT fields; compare them with specifications and standards; then archive the evidence after human-and-system review.
Review requirementsAssists review; it does not automatically determine acceptance
C7★Solution validated
Zero-trust remote access
No public service ports are opened on the firewall. The on-premises server establishes an outbound connection to a relay; external endpoints use the same relay for the handshake. Transport is end-to-end encrypted and the relay holds no keys.
TechnologyPrivate network + Permission matrix + Full audit trail
Review requirementsWe do not say “never leaves the environment” unless a fully offline architecture has been verified
C8Demonstration environment
Policy retrieval and approval traceability
The same question may produce different permitted evidence for different departments and roles. Access control is visible behavior, not a promise: out-of-scope requests are blocked or flagged and recorded in the audit log.
Review requirementsRetrieval, generation, edits, approvals and exports are logged
C9Solution validated
Past-project archive and experience reuse
Organise historical project files, decisions and technical approaches. Semantic retrieval connects related tasks while retaining sources, versions and conditions of use.
ValuePreserve institutional knowledge and decision records
C10Demonstration environment
Decision evaluation
We study how agents choose actions with incomplete information, competing goals and constraints. We connect observation, reasoning, tool use and feedback to examine decision grounds, execution traces and failure modes.
TechnologyStrategy comparison · Trace analysis · Human review
Review requirementsOur researchers define tasks and boundaries, examine decision grounds and actions, evaluate reliability and controllability, and decide whether actions should continue.
Interactive example: design changes+
Interactive · Bounded industry world model
Change one parameter, and see what moves
Working Definition
Enterprise world model = business ontology and state representation + real-world rules and constraints + state-transition and risk functions learned from historical data + scenario simulation and evaluation + human authorization
Move the slider to compare clear height, illustrative rule conflicts, estimated cost and schedule at different floor-to-floor heights.
This deterministic demonstration explains option comparison. It is not connected to project data, a regulatory database or a learned model.
Design change · Rule-based demonstrationDemo
Basement floor-to-floor heightBASE 3600mm3400 mm
±0−100−200−300−400mm
Structure and MEP allowance 1200mm→Actual clear height 2200mmNear demo threshold
Rule conflicts
0
Below the illustrative 2300mm coordination reserve.
Estimated cost
-CNY 500,000
Illustrative estimate in CNY, including earthwork savings and MEP changes; not a quote.
Estimated schedule
+12 days
Assumes 4 days per discipline plus 10 days for a threshold conflict.
Review path
Internal review
Actual approvals depend on the project, local codes and professional judgment.
Illustrative rules triggered
A height change triggers a structural reviewDemo assumption
Review MEP layout for a reduction of at least 150mmDemo assumption
Clear height is below the illustrative 2300mm reserveDemo assumption
Candidate options
▲Interactive demonstration: thresholds, costs and schedules are explicit assumptions, not code review, model predictions or a quotation. Selecting an option only compares it on this page; it does not execute, save or create an audit record. Real projects require current codes, jurisdiction and complete project conditions.
Capabilities & architecture+
Capability Stack · Modular capabilities
Models, tools and experimental systems
Configure models, retrieval services and execution tools around project materials, workflows, access permissions and professional requirements.
01
Private boundary
Private AI Core
Models run in a customer-controlled environment. Compute, network, identity, logs, backups and version rollback define the control plane; sensitive data remains within the authorized boundary and external connections are explicitly configured.
Reverse outbound connection— The customer-side server establishes the outbound connection; no public service port is opened on the firewall.
Relay does not hold keys— Transport is end-to-end encrypted; the relay does not hold keys needed to decrypt business data.
Identity- and project-based access— Apply least-privilege access by person, project and data scope, with isolation, revocation and periodic review.
Full-process audit— Connections, retrieval, file access, configuration changes and model calls leave queryable audit records.
External Port ScanSecured
Traditional VPN exposure
443/tcp open https
1194/udp open openvpn
3389/tcp open ms-wbt
22/tcp open ssh
4 PORTS EXPOSED
Reverse outbound connection
—
—
—
—
0 PORTS EXPOSED
The security boundary is established by architecture and validation
02
Model factory
Domain Model Factory
The same standard may be interpreted and applied differently across institutions, with much of that judgment not captured in structured policy. A general model does not automatically know institution-specific knowledge and standards; the work is to build governed enterprise knowledge and task capability.
Corpus governance— Clean, deduplicate, classify and de-identify content; create a permission-tagged corpus ledger as the foundation for model adaptation.
Continued domain pretraining (CPT)— Absorb domain terminology, regulatory language, writing conventions and abbreviations so the model understands the domain consistently.
Task fine-tuning (SFT / PEFT)— Separate adapters by task or specialty and evaluate, release, update and roll them back independently.
Preference alignment and evaluation— Build preference data from historical review decisions and real tasks, with reviewable offline and production evaluation sets.
Model Registry · v2.4.1Deployed
Corpus governance
1.8M documents
CPT
4.2B tok
SFT / PEFT
3 adapters
Preference alignment
12.6K pairs
Evaluation
PASS
Evaluation metric
Before adaptation
After adaptation
Field-extraction accuracy
71.4%
93.8%
Citation completeness
58.2%
97.1%
Miss rate
18.6%
4.2%
Manual-edit rate
62.0%
21.5%
Illustrative interface · Metrics are planning targets estimated for comparable organizations, not commitments
03
Agent workflow
Agentic Workflow
The system goes beyond question answering. An agent receives tasks, identifies files, retrieves evidence, calls tools, applies rules and produces results while escalating missing information, conflicts and high-risk matters to people. Every step can be traced, reviewed and rolled back when needed.
Source text can be verified— Link output to source text and identify the standard, version, clause and applicability marker.
No evidence, no delivery claim— A feature without a source, version or validation path is not represented as a delivered capability.
Explicit conflicts and gaps— Mark missing information, conflicting conditions and out-of-scope cases instead of forcing a definitive answer.
Risk-based human escalation— Route high-risk or low-confidence matters to human review, with the automation boundary visible in the trace.
Agent Trace · Task #A-2291Completed
01INGEST · 47 client materials → classify and tagOK
02PARSE · Scanned-document OCR → structured field extractionOK
03RETRIEVE · Compare current standards (4 national / 2 local)OK
04RETRIEVE · Retrieve the top five internal precedentsOK
05TOOL · Call the cost-estimation APIOK
06CONFLICT · Geotechnical data conflicts with planning conditionsEscalate
07MISSING · Client materials lack the site-boundary planEscalate
Represent business entities, states, constraints, actions and outcomes in a bounded industry world model. In regulatory, engineering, operating and risk scenarios, simulate option impacts first and give decision-makers an explainable evidence trail.
Scope and state space are explicit— Simulate only within defined business boundaries and state variables; do not extrapolate a bounded model into a real-world forecast.
Rule sources are traceable— Mandatory provisions, national/industry/local standards, local supplements, contract terms and internal practices retain source and version information.
Impact chains and evidence are visible— Trace the impact path across compliance, cost, schedule and approval; link every conclusion to its evidence and assumptions.
Decision and execution authority remain with people— Record options, selections and rationale as versioned audit entries; professionals make the final determination.
The earlier modules mainly support one-off task completion. Other work requires continuous monitoring: model clashes, drawing-version consistency, standards compliance, list changes and policy updates requirea rule-based monitoring mechanism that keeps running.
After configuring the monitored objects, rule set and notification policy, the agent runs on a schedule, scans continuously and reports only changes without repeated instructions.
Configure once, run continuously— Configure the scope, rules, alert levels and recipients together, with controlled updates later.
Report changes, not noise— Do not repeat the same issue; notify only on new, resolved or risk-level changes.
Alerts include evidence— Each alert includes the object ID, matched rule, model version and scan time for review.
Monitor continuously; do not replace decisions— The system detects and alerts; remediation, changes and sign-off remain subject to human approval.
11:40Scan B2 MEP model v2.7 · 1,102 componentsCompleted
11:41No new clashes; two findings carried forward from the prior runNo change
14:07Model version update detected v2.7 → v2.8Rescan
14:23Full-building rescan complete · original 3 clashes resolvedCompleted
New alerts this round 0Total escalations 17Reviewed 17Simulated data / illustrative interface
Reference Architecture · Private deployment
One diagram to make the boundary clear
Compute, network, identity, storage, model services, knowledge, agents, monitoring and audit need clear boundaries. This is one of the first deliverables before implementation.
InfrastructureINFRASTRUCTURE
GPU server clusterQuantization · Inference optimizationMulti-model schedulingResource and concurrency managementHigh-availability operationsBackup · Monitoring
Corpus ledgerCleaning · Classification · De-identificationPermission tagsPeople · Roles · ProjectsTraceable citationsSource text · Version
Intelligent workflowAGENTIC
Intake and identificationClassification · Field extractionRetrieval and executionEvidence · Rules · ToolsHuman reviewEscalate to a person
Business world modelWORLD MODEL
State graphEntities · States · ConstraintsScenario simulationChange → Impact chainOption comparisonEvidence · Confidence
Connected systems
Office / OA systems
Contracts / matter management
Project management systems
Risk / audit systems
Human approval · approve / edit / returnRecord the rationale; create a new version and audit entry
No publicly exposed inbound portsExternal scans cannot reach services inside the boundary
Reverse outbound connectionThe on-premises server connects out to a relay; the relay holds no keys; transport is end-to-end encrypted
API integrationConnect to existing office, contract, project and risk systems without replacing the current information environment
People stay in the loopHigh-risk and uncertain matters go to human review; judgment and sign-off remain with people
Scope & responsibilities+
Governance · Capability boundaries
Capability boundaries and public language
This is not marketing language. It is our public description of product capability, conditions of use and accountability boundaries. We commit only to what can be validated, delivered and audited; the remaining boundaries are stated just as clearly.
Capability area
What we can substantiate publicly
Explicit boundaries and claims we do not make
Data security
Deployment can remain within a customer-authorized environment; external calls are explicitly configured by the enterprise; transport is end-to-end encrypted and relays do not hold decryption keys. The actual boundary depends on architecture, network policy and security validation, as well as applicable law and agreed data-processing terms.
We do not make a blanket “never leaves the environment” claim. A no-egress claim requires a verified fully offline architecture and appropriate controls.
Accuracy
Citations are traceable, versions are identifiable and evaluations are reviewable; high-risk matters retain human review and final decision authority.
We do not promise to eliminate every hallucination or claim 100% accuracy. Model output is not a fact or professional opinion without appropriate review.
Pretraining and adaptation
We support continued domain pretraining, supervised fine-tuning, parameter-efficient fine-tuning, preference alignment, distillation, quantization and evaluation to help customers build manageable, iterative model assets.
We do not promise to train a foundation model from scratch for every enterprise. The appropriate level of adaptation depends on data, compute, the target task and validation results.
World Model
Within a clearly bounded business domain, simulate state changes and option impacts using enterprise rules, historical data and explicit assumptions for scenario analysis and decision validation.
We do not claim to understand or predict the entire real world, decide an enterprise’s future, or present a bounded simulation as a real-world forecast.
Agent
Within its authorization, the agent can call tools, execute steps, record the process and escalate uncertain or high-risk matters to people.
We do not claim that an agent can handle every business process without supervision. It does not bypass permissions, approvals or accountable owners, and it does not replace professional judgment.
Continuous learning
Feedback can update knowledge, models or workflows; changes move forward only after testing, approval, version control and rollback validation.
The system is not allowed to change its production behavior without approval. Continuous updating is not uncontrolled self-evolution.
Project delivery process+
From pilot to reliable operation
Define the goals for one specific task, then use measured results to assess value, implementation cost and readiness to scale.
Define scope and goals
Map the workflow, materials and tools. Use redacted examples to define task boundaries and evaluation criteria.
Build and evaluate a pilot
Evaluate output quality, processing efficiency and review procedures on representative tasks, alongside implementation effort.
Deploy and refine
Choose deployment around data and operational requirements, integrate with existing workflows and expand after stable operation.
Before you start+
FAQ
Before you start.
How do we start without a technical plan?+
Start with the task you want to improve, the current workflow and its main challenges. We assess whether AI is suitable before defining the approach and implementation scope.
Do I need to buy a server first?+
No. You can start with cloud compute or existing hardware. Deployment depends on the task, data requirements and budget.
Can AI integrate with our existing systems?+
We prioritize connecting existing systems. We first assess interfaces and permissions to identify what can connect and what needs adjustment.
How do we evaluate project value?+
Agree on measures such as output quality, search time, throughput and review effort. Use pilot results to assess benefits and costs before expanding the scope.
INTOSYN AI LABS / OPEN CONVERSATIONS
A research question. A new investigation.
Discuss research questions, experimental methods and application ideas with us, and identify directions worth investigating.
