Understand
Discover + Quantify
Map the workflow, owners, constraints and baseline worth improving.
Forward Deployed AI Engineering
AI systems that survive contact with the business.
Bigdoor Ai Labs works inside real workflows to identify high-value AI opportunities, build the systems, connect them to existing operations and move them into production.
Deployment mapproduction path
Business context
AIsystem
Operating outcome
01Action
02Escalation
03Measure
workflow connectedevaluation online
BuiltMonitorMyGEOAI visibility platform
ExperienceB2B + AIoperator-led delivery
FocusForward Deployed AIworkflow → production
Method7 disciplinesdiscover → compound
Where deployment stalls
AI projects rarely die in the demo. They die on the way to production.
Choose the situation that sounds most familiar.
01
02
03
We know AI matters. We do not know where to start.
Too many ideas, no ranked workflow and no defensible first deployment.
Find the first workflow →The prototype worked. Production did not.
Integration, evaluation, reliability, security or ownership is still unresolved.
Close the deployment gap →We have an AI team. Delivery is still slow.
Internal capability exists, but complex implementation needs embedded execution.
Add deployment capacity →Two ways to start
Start with evidence, not a transformation programme.
~4 minutes
Take the diagnostic →
AI Deployment Readiness Diagnostic
Test one opportunity across workflow, data, integration and adoption conditions.
Working session
Discuss a workflow →
Map a workflow with us
Bring one expensive, repetitive or decision-heavy workflow. We will start with where AI belongs and where it does not.
The Forward Deployed model
We do not hand over the AI strategy and disappear.
Forward deployment keeps discovery, architecture, implementation, evaluation and adoption attached to the same operating outcome.
Read the field guide →Design
Architecture + evaluation
Choose the system boundaries, integrations, controls and definition of good.
Deploy
Build + integrate
Put the system inside the actual operating workflow and instrument it.
Operate
Adopt + compound
Remove friction, measure outcome and use deployment learning to find the next opportunity.
Under the hood
An AI agent is useful when it can act inside a controlled workflow.
Example: a lead-verification workflow. The important engineering is not the chat window. It is context, tools, permissions, exception handling and measurement.
MODEL LLM selected for taskSYSTEMS CRM · voice · databaseCONTROLS permissions · evals · escalationOBSERVABILITY quality · cost · latency · failure
01
Lead enters CRMStructured trigger
02
Context enrichedCompany + contact data
03
Voice agent contacts leadTask-specific interaction
04
Qualification decisionRules + model judgement
05
Uncertain case escalatesHuman review path
06
CRM updatedOutcome written back
07
Result evaluatedQuality + business metric
Selected work
Proof should look like a system, not a paragraph of adjectives.
Until client work can be published responsibly, products and technical artefacts carry the evidence.
Product · AI visibility
MonitorMyGEO
A platform for measuring how brands appear across generative search and answer engines, then turning observations into actions.
Visit MonitorMyGEOMonitorMyGEOVisibility workspace
AI visibilityOBSERVE
Answer enginesMentionsCitationsCompetitors
Action queueEntity gapsSource gapsContent gaps
Website Launch Auditor
Structured launch-readiness analysis across engineering, UX and discoverability.
Architecture before theatre
Workflow maps, evaluation plans and system diagrams become public when they can be shared responsibly.
Services
Choose the deployment problem, not the fashionable label.
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Forward Deployed AI
Own one high-value use case from workflow discovery through production.
AI Agents
Agents with jobs, tools, boundaries, evaluation and escalation.
Enterprise AI
AI connected to existing systems, data, identity and governance.
AI Automation
Rules where rules work; model judgement where judgement is required.
Founder-led
Business operator turned AI builder.
Bigdoor Ai Labs is led by Vishesh Allahabadi, combining years of B2B operating experience with postgraduate work in AI and machine learning.
View founder profile →BusinessB2B sales, distribution and operating workflows
TechnicalAI & ML postgraduate work
BuildProducts, automation and applied AI systems
NowForward Deployed AI Engineering
Deployment Library
Build authority by publishing the work behind the work.
Field guide
What is Forward Deployed AI?
A practical definition, lifecycle and comparison with adjacent delivery models.
Read guide →MethodFrom workflow to production
The operating method behind Bigdoor Ai Labs deployments.
Explore →ResearchEvidence with methodology attached
Original work will ship with data, limitations and method.
Research →