Snowflake 
BigQuery 
Postgres 
dbt 
Fivetran 
Looker Analytics spend · 2026
$420 B
Investment in decision support is massive
Global spending on big data and analytics signals serious investment in decision infrastructure.
Source: IDC via Bismart, 2026
SERVICE 06 · DATA AND PLATFORM
The data layer your AI actually needs
Techtiz builds the data foundation AI depends on: pipelines, warehouses, and observability with lineage and monitoring designed in rather than bolted on. We score your readiness before building and deliver in phases you can stop between.
Readiness scored first Phased, stoppable delivery Lineage and monitoring built in
what we built
Snowflake BigQuery Postgres dbt Fivetran Looker
THE CATEGORY PROBLEM
Every agent project becomes a data project.
The pattern repeats. An agent pilot works in the demo and fails in production because the data underneath is fragmented, stale, or unowned. Data readiness is consistently the top-named blocker in 2026 AI research, ahead of model capability by a wide margin. IDC finds organisations with high data readiness convert proofs of concept to production several times more often than those without. We build the layer first, or fix it mid-flight when a pilot has already stalled on it.
4 of 33
Proofs of concept reach production
IDC with Lenovo, 2025
$12.9 M
Average annual cost of poor data quality per organization
Gartner, 2026
47 %
New data records that contain at least one critical error at entry
MIT Sloan, 2026
3 %
Companies whose data meets basic quality standards
Harvard Business Review, 2026
BEFORE ANYTHING GETS BUILT
The readiness score, before the build
The audit produces a scored map across 6 dimensions, each with the specific finding behind the score. You get the map whether or not you build with us.
Completeness
What is missing, and does it matter for the use case.
Freshness
How stale is the data at the point an agent would read it.
Access
Can a system get it without a human exporting a spreadsheet.
Lineage
Can you trace a number back to its source.
Ownership
Who is accountable when a field is wrong.
Sensitivity
What is personal, regulated, or contractually restricted.
What we built
Ingestion and transformation pipelines, plus lineage so you can trust what an agent reads .
A warehouse or lakehouse sized to the company, monitoring so you find out a pipeline broke before the agent does, and reporting layers your team can actually query.
Governance moves into the pipeline, not a policy filed after launch: PostgreSQL or a managed warehouse , incremental and idempotent loads, schema contracts at ingestion boundaries, freshness and volume tests on critical tables, alerting on test failure rather than on dashboard silence.
what we built Snowflake BigQuery Postgres dbt Fivetran
Where the sector is heading
Data systems are shifting from storage to governance and observability.
Governance · 2026
As code
Policy moves into the pipeline
Governance as code automates policy enforcement directly inside data pipelines.
Source: Bismart, 2026
Incident time · 2023
166 %
Data incidents take far longer to fix
Time to resolve a data quality incident jumped to 15 hours, demanding better monitoring.
Source: Monte Carlo, 2023
PHASED, SO YOU CAN STOP
Data work is where budgets disappear
Data work is where budgets disappear, because the scope is genuinely open-ended if nobody closes it. We phase it. Each phase has a named outcome, a fixed scope, and a stopping point where the work delivered so far still stands on its own. You decide at each boundary whether the next phase is worth it. If the answer after phase 1 is that your first use case is now unblocked and phase 2 can wait 2 quarters, that is a good outcome and we will say so.
ACCEPTANCE EVIDENCE, WRITTEN FIRST
How you will know it worked
Acceptance evidence is written before the phase starts. "The pipeline runs" is not acceptance evidence. "This number reconciles to source and the alert fires when it does not" is.
Named tables passing freshness and volume tests, on a schedule.
A specific query or agent retrieval returning correct results against a graded set.
Lineage traceable end to end for a named metric.
Alerting proven by a deliberate induced failure.
WHERE YOUR DATA GOES
Where the data physically sits
Pipelines and storage run in your cloud accounts, under your billing, unless you ask otherwise.
Engineering access is from Lahore, Pakistan, scoped per environment, logged, and revoked at exit.
Where a data class must remain in a jurisdiction, we design for it and state what our side touches.
Non-production work runs on de-identified or synthetic data wherever the use case allows it, and we tell you when it does not.
PII handling: classification first, then masking and access controls at the pipeline level, then a written data map you can hand to counsel or to a customer’s security reviewer.
The data map is a deliverable, not an internal document. If you cannot forward it, it is not finished.
SIZED TO THE COMPANY
Warehouse or lakehouse at your size
For most companies of 50 to 500 people, a warehouse on PostgreSQL or a managed equivalent is the right answer until data variety genuinely forces the question. Lakehouse architectures solve a problem most mid-market companies do not have yet, at an operational cost most mid-market teams cannot staff. We size to the company, not to the vendor deck. If your situation genuinely calls for a lakehouse, the audit will show why in your own numbers.
WHAT WE WILL NOT DO
The no list
Build a warehouse before there is a named use case for it. A warehouse with no consumer is a monthly bill.
Migrate a platform because the current one is unfashionable.
Move regulated data across a boundary we should not move it across.
Ship a pipeline without tests and alerting. An unmonitored pipeline is a future incident with a delay fuse.
Ownership
Your repositories, your IP, assigned on payment, from day 1.
Exit
30 days notice on anything monthly. Handover runbook included, not quoted separately.
Overlap
4+ hours of daily overlap with your working day, contractual.
Boundary
Work performed in Lahore by Techtiz personnel under a US-formed Wyoming LLC. Regulated data classes stay on your side and we design around that.
Named engineer
1 senior engineer accountable for the estimate, on the first call.
Sourced claims
Every non-obvious statistic on this site carries a named third-party source and a date.
For U.S. SLED prime contractors
Reporting and analytics layers, behind the prime.
For SLED scope under NAICS 518210, we build governed analytics layers as your subcontractor, with PII handled to standard, never facing the agency.
NAICS 518210 541512 541511
See SLED Subcontracting NDA-first, subcontract-only. We work behind the prime, under your brand. We do not pursue prime contracts and we never face the agency.
Parallel analytics layer. ELT runs without altering operational databases, so live applications stay fast.
Lineage you can show. Transparent data lineage, tested pipelines, and strict handling of personally identifiable information.
FAQ
The questions you were going to ask
How long before this unblocks anything?
Phase 1 is scoped to unblock 1 named use case. If the audit says that takes longer than a quarter, you hear it in the audit, before you commit to delivery.
Where does our data go?
Your cloud accounts, your billing. Access from Lahore, scoped and logged, revoked at exit.
Will this actually fix our AI problem?
The audit answers that specifically, including the case where data is not your blocker and something else is. That answer is worth the audit on its own.
Can you fix pipelines you did not build?
Yes, and sometimes the recommendation is repair rather than rebuild, which is the less profitable answer for us.
Warehouse or lakehouse?
Usually a warehouse at your size. We size to the company, not to the vendor deck.
Written by Adnan Naeem, Chief Technology Officer
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Score your data before you build on it
Tell us the AI use case that is stalled. We will tell you whether data is the reason.