Our HubSpot setup no longer fits how we sell.
I can redesign the CRM structure, data and connections around the business process, so people can act on a coherent record instead of reconciling conflicting versions first.
CRM, Data & IntegrationsB2B revenue and marketing operations
Revenue systems built to carry the work forward.
I design and build the CRM, data and workflows behind B2B revenue teams, connecting the commercial process to the systems that run it. That includes deciding what rules can automate, what AI agents can research or prepare, and where people need to make the call.
You work directly with me, from understanding the problem through design, build and testing.
Osama Tahir · 6+ years in B2B operations · HubSpot and connected systems · Dublin
The operating environment
Where the work I build sits
An enquiry or relevant signal arrives
SystemA form, a reply, a change on an account
CRM and available context are assembled
SystemRecord, history, prior activity
Agreed ownership and workflow rules apply
Deterministic ruleRouting, lifecycle, exceptions
AI prepares context where that helps
AI agentResearch, classification, preparation
A person reviews or responds
PersonThe commercial judgement stays human
The record and next step are updated
SystemOwner, action and reason are visible
Outcome
The enquiry reaches an accountable next step.
The operating environment around it
- CRM and data
- Lifecycle and automation
- Rules
- AI agents
- People
One illustrative path through the environment. I design and build the parts it passes through, and decide which of them a rule, an agent or a person should hold. Engagements differ.
What needs to work differently?
Interest comes in, but the next step is inconsistent.
I can connect lead capture, qualification, ownership and follow-up, so an interested prospect reaches someone who understands why they have been handed over and what should happen next.
Lifecycle, Lead Management & AutomationPeople are still doing the research and coordination between our tools.
I can build a defined workflow that prepares context, applies rules and brings in AI reasoning where it helps. The aim is to let someone assess the next step without restarting the investigation.
GTM Engineering & AI WorkflowsIf you need senior hands-on capacity for a defined workstream, I can work directly with your team or through a consultancy.
Ways to work togetherFeatured partnership
Strategic Computing
Connecting specialist expertise to a usable buyer journey.
For this Dublin-based negotiation and IT procurement consultancy, the work began with the path from outreach to a conversation. I worked on HubSpot structure, contact intelligence, structured outreach, a landing page and lead asset, and bringing existing customer proof into that journey.
The purpose was practical: give interest somewhere useful to go, connect it to follow-up, and keep the setup proportionate to a founder-led business.
Read the published case study(opens in a new tab)“Working with Osama brought a lot more structure and direction to what I was trying to build. He helped make sense of the tools, the outreach, and the bigger go-to-market picture, and he was especially strong at bringing HubSpot and AI into the process in a way that was practical and useful.”
Illustrated journey
- 01Outreach
- 02Useful content and customer proof
- 03Enquiry or consultation
- 04CRM context
- 05Follow-up
Inside the work
A signal is a reason to look. The evidence determines what follows.
Account Intelligence is an AI-assisted account research workflow. It gathers public evidence and available account context, then returns a structured brief for a person to read before deciding whether to act. Here is an example where the available evidence left an important question unresolved.
Inspection points
The listing is visible. The requirement is unconfirmed.
The question
Does a published role indicate a current CRM change that this business might need help delivering?
What the research found
An external job listing described a CRM-related role. The company’s own careers page did not list it at the time of research. The advert mentioned several CRM platforms as examples, without identifying which one the company had selected.
What remains uncertain
Whether the requirement is still open, which platform is involved, and whether external help is relevant.
Research recommendation
Check whether the requirement is still live before investing more effort.
This is the research output’s recommendation, not a recorded commercial decision. The ACT, HOLD or no-meaningful-signal call remains unrecorded in this example.
Why uncertainty remains
External listing
Observed fact
The role remained visible on a job board.
Company careers page
Observed fact
The role was absent when checked.
Platform evidence
Inference limit
Examples of experience required, not confirmation of the installed system.
These observations do not establish a live project or buying intent.
Provenance: adapted from a research output dated 29 August 2026. The presentation here is reconstructed and is not a live application. Source excerpts are described by type rather than linked, because a source-linked version could re-identify the account.
A research recommendation is not a recorded decision
Research recommendation
Check whether the requirement is still live before investing more effort.
Recorded commercial decision
Not established in this output.
The workflow supports three possible human decisions:
- ACT
- Enough relevant evidence for a specific next step.
- HOLD
- An unresolved question or timing issue makes action premature.
- No meaningful signal
- The evidence does not support a relevant buying situation.
What the output actually documents
This example brings together a research question, public sources and a written assessment. No CRM read, automated rule execution or write-back is evidenced in the selected output.
An agent works with the context, permissions and data made available to it. I design those inputs alongside the task it is being asked to perform.
Illustrative workflow
Design explanation, not an execution trace
In a connected workflow, I design those steps around the job: what context can be accessed, which checks follow fixed rules, what an agent researches, who decides and what may return to the system.
01
Input
System
Result
The task exists with its source recorded.
Behaviour and next step
Behaviour
A signal or named account enters the workflow.
Next step
Assemble what is already known.
02
Available context
System
Result
A starting context, with gaps visible.
Behaviour and next step
Behaviour
Accessible account records and prior activity are gathered.
Next step
Check whether the account qualifies for research.
03
Eligibility
Deterministic rule
Result
Same input, same outcome, every time.
Behaviour and next step
Behaviour
Fixed criteria decide whether this account proceeds.
Next step
Hand an eligible task to research.
04
Research
AI agent
Result
Observations, each retaining its source.
Behaviour and next step
Behaviour
Permitted sources are read against the research question.
Next step
Structure what was found.
05
Structured evidence
AI agent
Result
An assessment that keeps its unknowns visible.
Behaviour and next step
Behaviour
Observations are separated from inference, with validation checks where designed.
Next step
Present it for review.
06
Presentation
System
Result
A readable output in a consistent shape.
Behaviour and next step
Behaviour
The assessment is delivered where the person already works.
Next step
A person reads it.
07
Commercial decision
Person
Result
A decision with a named owner.
Behaviour and next step
Behaviour
Act, hold, or record no meaningful signal.
Next step
Any permitted action follows the decision.
08
Permitted action
System
Result
The record shows what happened and why.
Behaviour and next step
Behaviour
Only actions explicitly allowed by design are carried out.
Next step
The outcome returns for checking.
09
Verification
Rule and person
Result
Weak cases stay visible instead of going quiet.
Behaviour and next step
Behaviour
Rules flag exceptions and thin evidence; a person reviews them.
Next step
Findings inform the next design change.
Experience behind the build
I have spent more than six years working across B2B marketing operations and revenue systems, most recently as Associate Director, Marketing Operations at 2X.
My experience includes lifecycle and qualification design, CRM workflows, connected tools and the operating decisions behind marketing-to-sales hand-offs.
Selected ExperienceBuilt in the Lab
The Lab is where I build and test systems around real operating questions. Some support my own work; others test an approach before it is considered for client delivery.
Trigebra
A HubSpot diagnostic build that turns API and configuration evidence into findings using deterministic checks, constrained synthesis and QA.
Inspect the buildAccount Intelligence
Research and evidence prepared for a human commercial decision. Explore the selected output above.
Inspect the exampleSystems I work across
HubSpot is my deepest platform experience. The work usually crosses CRM, data, enrichment, analytics, web, automation and AI tooling as well.
- HubSpot
- Salesforce
- 6sense
- Clay
- Apollo
- Claude
- OpenAI
- GA4
- WordPress
Thinking shaped by the work.
Inside an AI-Native Marketing Org: What the Tools Couldn’t Fix
On the definitions and ownership that tools alone cannot resolve.
Read on LinkedIn(opens in a new tab)Your HubSpot + Salesforce combo was supposed to scale marketing. So what happened?
A co-authored piece on the operating work between connected platforms.
Read on 2X(opens in a new tab)Tell me what happens today, and what needs to happen instead.
You do not need a finished scope. Bring the system, workflow or commercial task you are trying to improve.
We can establish where I can help and whether the next step is to scope the work, deliver a defined change or provide senior capacity.