Permanent analytics operations

Analytics that compounds. Run by agents and specialists.

Specialists and agents that run your measurement, optimization, and experimentation — continuously, as part of your operation, not as a project.

In operation since 2009 · 200+ enterprises served · ~4 year avg. tenure · Google Premier Partner
Book a 30-min briefing → See how it runs
Operation in progress 05:17 IST · today
Enterprise · BFSI · 14 properties OP-04,217
04:12
Anomaly detectedCheckout Δ −8% vs 30d baseline
Agent
04:17
Funnel isolatedLocalized to single payment route
Agent
04:23
Hypothesis draftedThird-party timeout pattern
Agent
Now
Queued for specialist reviewWindow opens 09:00 IST
Pending
Active operationsRunning now
Avg tenure~4 years
Operating since2009
17 years
running analytics for enterprises
200+
enterprises served
~4 years
average tenure
Google Cloud Partner Google Premier Partner
Royal Enfield TVS Motors SonyLIV HDFC Life BookMyShow Asian Paints ICICI Prudential PharmEasy ET Money Dot & Key Titan Eye+ MX Player Royal Enfield TVS Motors SonyLIV HDFC Life BookMyShow Asian Paints ICICI Prudential PharmEasy ET Money Dot & Key Titan Eye+ MX Player

Built for digitally complex enterprises.

01

Multiple digital properties

Web, app, and offline touchpoints that need unified measurement. Not one website. An ecosystem.

02

Analytics that's outgrown your team

Your internal team is good. They're also stretched across too many properties, too many stakeholders, and too many tools. The operation needs permanent capacity, not another project.

03

Strategy stays with you, execution needs a system

You set the direction. You need an operation underneath that runs without being managed day-to-day.

01 · How it runs

Three actors. One continuous operation.

Agents, specialists, and your team — each with a defined role, operating at different cadences, producing work the others consume.

Every minute
Agents
Autonomous. Never off. No tickets.
Owns
Baseline monitoringDrift, anomalies, quality — checked continuously
Pattern detectionSurfaces what changed and where
Hypothesis draftingIsolates probable cause before a human looks
Automated workflowsReporting, alerting, segmentation — runs without asking
Produces →
Anomaly logs, draft hypotheses, automated reports, quality scores
→
reviewed daily
Every day
Tatvic Specialists
Context. Judgment. Accountability.
Owns
ValidationConfirms or rejects agent hypotheses with business context
InstrumentationTracking, schemas, identity — maintained and evolved
Experiment designTests scoped to revenue, not vanity metrics
Stakeholder deliveryFindings packaged for decisions, not exploration
Produces →
Validated findings, recommendations, experiment results, performance reports
→
delivered weekly
Every week
Your Team
Strategy. Priorities. Decisions.
Owns
Strategic directionWhat matters this quarter, what to measure next
PrioritizationWhich findings to act on, which to park
DecisionsGo/no-go on experiments, scope changes, new properties
Feedback loopPriorities shape what agents watch and specialists deliver
Produces →
Direction that shapes the next cycle of agent monitoring and specialist work
Agents
Continuous · autonomous
Specialists
Daily · contextual
Your team
Weekly · strategic

The operation compounds — as engagement matures, more work shifts from specialist to agent. Your team's role stays the same: set direction.

02 · What we run

Three areas, one continuous operation.

Analytics, optimization, and cloud infrastructure — run as a single embedded operation, not three separate services.

01
Analytics

We run your analytics.

"Is my data correct? What is it telling me?"

Measurement, dashboards, ad-hoc analyses, insight generation. GA4, GTM, BigQuery maintained continuously — so your data never drifts and insights surface without being asked for.

How we run Analytics →
02
Optimize

We run your experimentation.

"How do I improve what the data is showing me?"

Hypotheses generated from behavioral data. Tests designed, launched, and measured against revenue — continuously, not quarterly.

How we run Optimize →
03
Cloud

We run your data infrastructure.

"Can you build and maintain my data infrastructure?"

GCP projects, BigQuery pipelines, cost management, deployments. Operated by specialists who know your environment better than any new hire could.

How we run Cloud →
03 · How we engage

A permanent operation, not a project.

Every engagement includes specialists, agents, and a dedicated operation. Most enterprises converge on this model.

Flagship engagement

Managed Operations

Tatvic runs your measurement, optimization, and automation as a permanent operation. You set strategy. We run everything underneath — specialists and agents embedded in your environment, operating continuously.

Contract
Own contract · 12 mo min.
Team
Dedicated specialist pod
Agents
Embedded in your environment
Shape
Not a project. An operation.
−26%
Cost per lead
−39%
CPL on predictive
campaigns
150+
Experiments
run
"They created a custom solution that reduced CPL by 26% and 39% for predictive and lead scoring campaigns. Tatvic's expertise made a significant impact on our campaign efficiency."
JC
Jatin Chikara
VP Marketing, Royal Enfield
Most enterprises start with a single domain — analytics, optimization, or cloud — and expand as the operation proves itself. Every engagement converges on the same model: permanent, embedded, compounding.
04 · Proof

The measure of the work isn't what we delivered. It's how long it's been running.

Auto · Motorcycles
Royal Enfield
6y 2m · Since 2019
Read case study →
BFSI
Bajaj Finance
5y 3m · Since 2020
Engagement details →
Media · OTT
Sony LIV
5y 1m · Since 2020
Engagement details →
Auto
TVS Motors
4y 8m · Since 2021
Engagement details →
Travel
Cleartrip
3y 2m · Since 2022
Engagement details →
+16 more enterprises under continuous operation

If your analytics operation
should be permanent, it should run like one.

30 minutes. We'll tell you whether what we do is relevant to what you're trying to solve — and whether we're the right fit.

Book a 30-min briefing →
Home→ What we run→ Analytics
Analytics operations

An embedded team that runs your analytics. Permanently.

Agents and specialists running your measurement, dashboards, insights, and data infrastructure — as a continuous operation, not a project.

Book a 30-min briefing → Meet the embedded team
What the operation covers
GA4
Google Analytics
Events, properties, audiences
GTM
Tag Manager
Containers, triggers, variables
BQ
BigQuery
Pipelines, exports, warehouse
LS
Looker Studio
Dashboards, reports
FB
Firebase
App analytics, events
CM
Consent Mode
Privacy, CMP, compliance

All maintained continuously. Not set up and handed off.

The problem

The project model is why your analytics keeps breaking.

Instrumentation decays

You launch GA4. It's accurate on day one. Six months later, a dev team ships a new checkout flow, nobody updates the tracking, and your conversion data is wrong for weeks before anyone notices.

Dashboards go stale

Someone builds a dashboard. It answers last quarter's questions. This quarter's questions are different. Nobody rebuilds it. Stakeholders stop looking. Data becomes decoration.

Insights require tickets

Every question requires a request. Every request requires context-setting. Every answer arrives too late to act on. The analytics function becomes a bottleneck, not an accelerant.

These aren't skill problems. They're operating model problems. Projects end. Operations don't.

6
starting agent skills per role
12+
skills by month 9
60 days
to calibrate to your stack
24/7
agent monitoring
01 · The embedded team

Two agent-human pairs. Calibrated to your stack.

Every analytics operation runs on two specialist pairs. Each pair has an agent that works continuously and a human who owns judgment, context, and delivery.

Agent · always on
Technical Analyst Agent
Starting skills — calibrated to your stack in 60 days. New skills added as the operation learns your business.
Event schema creation Tag auditing GTM container health Data sanity checks Implementation drift Release-gate QA
New skills added as the operation matures
→
Drafts Validates Delivers
Human · always in charge
Technical Analyst

Architecture decisions

Schema, stack, and measurement design choices that shape the system.

Complex implementations

Migrations, custom events, and edge cases that need judgment.

Sign-off & exceptions

Final review; judgment when rules don't fit the situation.

Agent · always on
Digital Analyst Agent
Starting skills — calibrated to your stack in 60 days. New skills added as the operation learns your business.
Funnel drop-off analysis Anomaly detection User journey analysis Form-fill analysis Report drafting Trend monitoring
New skills added as the operation matures
→
Drafts Validates Delivers
Human · always in charge
Digital Analyst

Business context

Frames the question behind the data; knows what matters to your business.

Insight narratives

Connects signal to strategy, in your voice and with your priorities.

Final sign-off

Validates, refines, and closes the loop with your stakeholders.

02 · What it looks like

A week inside a running analytics operation.

The embedded team in action — every entry is a real output, not a capability description.

Operation log · Analytics Week of 14 Apr 2026
Monday
06:12
TA Agent
Add-to-cart rate −34% on mobile. GTM container update isolated. via data sanity checks
09:30
Tech Analyst
Root cause confirmed — broken dataLayer push. Tag patched. Stakeholder notified with impact summary.
14:15
DA Agent
Cross-device sessions +18% since campaign launch. Segments isolated. via trend monitoring
Tuesday
10:00
Dig. Analyst
Insight brief delivered: campaign driving consideration, not conversion. Recommendation — adjust mobile CTAs.
Wednesday
11:30
Dig. Analyst
Q2 campaign dashboard updated — new regional breakdown live per brand team request.
11:45
TA Agent
Dashboard data source validated. Filters tested. All passing. via data sanity checks
Thursday
02:14
TA Agent
3 tags firing before consent on DE property. CMP config change detected. via tag auditing
08:40
Tech Analyst
Consent violation confirmed. Consent mode patched. Resolved before DE business hours.
Friday
11:00
Your Team
Weekly sync — 25 min. Three items closed. Next week: instrument new loyalty program flow.
This is what a permanently running analytics operation produces — every week, without being asked.
03 · The compounding effect

Context accumulates. Every output gets smarter.

The longer the operation runs, the more it knows — about your data, your stakeholders, your business. That intelligence compounds.

Months 1–3

Learning your business

Thresholds are generic — agents flag anomalies using industry defaults
Reports use standard formats — functional, not shaped for your team
Every call needs context-setting — you explain what matters and why
What this feels like
Useful, but generic. The operation doesn't know you yet.
Months 4–8

Context takes hold

Thresholds are yours — Sunday dip is normal, Wednesday dip triggers an alert
Reports match your stakeholders — CMO gets a different brief than product
New agent skills deployed — consent auditing, campaign-specific anomaly rules
What this feels like
Less explaining, more acting. The operation starts anticipating.
Months 9+

Insights before you ask

Insights surface unprompted — the operation knows what matters this quarter
Agent skill library has doubled — skills built for your specific patterns
New hires get onboarded by the operation — context lives in the system
What this feels like
An analytics function that knows your business as well as your best people — and never leaves.

This is what tenure produces. Not continuity — accumulated context, expanded skills, and an operation that knows your business better every month.

Your analytics shouldn't need a project every time something breaks.

30 minutes. We'll look at how your analytics is currently run, where it's breaking, and whether a permanent operation makes sense.

Book a 30-min briefing →
Home→ What we run→ Optimize
Optimization operations

An embedded team that finds what to fix and designs how to test it.

Continuous analysis and hypothesis design — running as a permanent operation, not a quarterly CRO audit.

Book a 30-min briefing → Meet the embedded team
The optimization loop
01
Analyze
Digital Analyst
Funnel analysis Heuristic eval
02
Hypothesize
CRO Specialist
Hypothesis gen Mockups Prioritization
↻
Learning loop: Test results feed back into analysis. Every cycle starts with more evidence than the last.
The problem

Project-based CRO is why your conversion rate plateaus.

Learnings walk out the door

A CRO specialist runs 20 tests. They leave. The next person starts from zero — same hypotheses, same mistakes, no memory of what was already tried and why it failed.

Analysis and testing live in silos

The analyst finds friction. The CRO person builds a hypothesis. Nobody shares context. Half the signal is lost in handoffs between people who don't work together daily.

Testing is seasonal, not systematic

You run a burst of tests before a sale. Then nothing for months. Conversion rate improves in Q4, decays by Q2. There's no compounding because there's no continuity.

The bottleneck isn't testing velocity. It's the absence of a learning loop that persists across people, quarters, and business cycles.

5
agents across two phases
2
specialists running the loop
90 days
to calibrate to your funnels
Every test
feeds the learning library
01 · The embedded team

Two specialist pairs. Five agents. One loop.

The optimization operation runs in two phases — analyze and hypothesize — with test results feeding back into the next cycle.

Phase 01 · Analyze
Agents · always on
Funnel Analysis Agent
Funnel drop-off detection Segment comparison Form abandonment Trend monitoring
Heuristic Evaluation Agent
UX heuristic scoring Page-level audit Information scent Cognitive load flags
New skills added as the operation matures
→
Surfaces Quantifies Packages
Human · always in charge
Digital Analyst

Diagnoses the "why"

Interprets data in business context — connects drop-offs to UX causes, not just numbers.

Qualitative review

Session replays, heuristic judgment, and user behavior patterns the agents flag but can't interpret.

Packages the brief

Clear problem statements with evidence — ready for the CRO specialist to act on.

Phase 02 · Hypothesize
Agents · always on
Hypothesis Generation Agent
Hypothesis drafting Historical test lookup Pattern matching
Mockup Rendering Agent
Variation wireframes Copy alternatives Layout options
Prioritization Agent
Impact estimation Effort scoring Rank & sequence
New skills added as the operation matures
→
Drafts Renders Prioritizes
Human · always in charge
CRO Specialist

Hypothesis design

Applies conversion psychology, UX heuristics, and pattern recognition to shape what gets tested.

Test strategy & sequencing

Decides priority, what to test next vs. later, and what not to test at all.

Variation sign-off

Reviews copy, layout, and interaction design before handing off the test spec.

02 · What it looks like

A week inside a running optimization operation.

The embedded team in action — every entry is a real output, not a capability description.

Operation log · Optimize Week of 14 Apr 2026
Monday
07:15
Funnel Agent
Checkout funnel: mobile add-to-cart → payment drop-off at 62%. Segment isolated — new users, organic. via funnel drop-off detection
08:30
Heuristic Agent
Payment page scored. Information scent violation: shipping cost absent until final step. Cognitive load high — 3 form sections visible at once. via UX heuristic scoring
10:30
Dig. Analyst
Session replays reviewed. Root cause confirmed: shipping cost surprise at payment step. Analysis brief sent to CRO specialist.
Tuesday
09:00
Hypothesis Agent
3 hypotheses drafted. Historical lookup: similar test Q3 2025 — inline cost preview lifted CR 8%. via historical test lookup
10:15
Mockup Agent
Two variation mockups rendered — tooltip on PDP vs. inline shipping banner below price. Both mobile-first. via variation wireframes
10:45
Priority Agent
Inline banner scores higher — projected +9–13% payment completion on mobile. Effort: low. Ranked #1. via impact estimation
Wednesday
09:30
CRO Specialist
Hypothesis selected: inline shipping banner on PDP. Tooltip rejected — too easy to miss on mobile. Variation copy refined. Test spec finalized and handed off.
Thursday
11:00
Funnel Agent
Next cycle: PDP bounce rate spike on "Accessories" — 3-day trend, +22% vs. baseline. Flagged. via trend monitoring
14:00
Heuristic Agent
Accessories PDP scored. Image-to-text ratio poor. CTA below fold on mobile. Product comparison absent. via page-level audit
Friday
11:00
Your Team
Weekly sync — 20 min. Shipping-banner test spec delivered. Accessories PDP issue triaged for next cycle.
11:30
Learning Loop
Last cycle's checkout test results logged. Learning library updated: price-transparency tests on mobile yield 7–12% lifts on this site.
This is what a permanently running optimization operation produces — every week, without being asked.
03 · The compounding effect

The learning library never resets.

When a CRO specialist leaves, their test history and intuition leave with them. When the system has a learning library, every test makes the next one sharper.

Months 1–3

Calibrating

~8
tests in the learning library
~30%
win rate — learning what works here
Generic
hypotheses from best practices
Months 4–8

Patterns emerge

~25
tests — enough to see site-specific patterns
~45%
win rate — the operation knows what doesn't work
Informed
hypotheses built on prior results
Months 9+

Compounding

40+
tests — a deep evidence base for this site
~55%
win rate — each test builds on the last
Precise
hypotheses from your data, your audience

A new CRO specialist starting fresh would be back at Month 1. A permanent operation is already compounding — and every month makes the next one smarter.

Your optimization shouldn't reset every time someone leaves.

30 minutes. We'll look at how your experimentation is currently run, where learning is being lost, and whether a permanent operation makes sense.

Book a 30-min briefing →
Home→ What we run→ Cloud
Cloud operations

An embedded team that runs your data infrastructure — so you never think about it.

GCP projects, BigQuery pipelines, cost management, and deployments — run as a permanent operation, not tickets to a vendor.

Book a 30-min briefing → Meet the embedded team
What the operation covers
GCP
Google Cloud Platform
Projects, IAM, billing, access management
Managed
BQ
BigQuery
Pipelines, ETL/ELT, scheduled jobs, queries
Managed
DP
Deployments
Cloud Functions, App Engine, prebuilt solutions
Managed
₹
Cost Operations
Billing RCA, optimization, TCO preparation
Monitored

All maintained continuously. Not set up and handed off.

The problem

Project-based cloud management is why your infrastructure keeps surprising you.

Infrastructure knowledge lives in one person's head

GCP project structure, IAM permissions, pipeline logic — one engineer knows it all. They leave. Your team spends months reverse-engineering your own infrastructure.

Cost creep is invisible until it's a crisis

BigQuery costs escalate without anyone watching. Orphaned resources accumulate. By the time finance flags it, you've burned budget for months on queries nobody optimized.

Pipelines rot without maintenance

Scheduled jobs fail silently. Schema changes upstream break transforms downstream. Nobody patches. By the time someone notices, the data your analytics depends on is weeks stale.

These aren't cloud engineering problems. They're operating model problems. Nobody is watching continuously — so everything degrades until it breaks.

3
agents across infrastructure, pipelines, and deployments
1
cloud engineer running the operation
60 days
to map your environment
24/7
agent monitoring
01 · The embedded team

Three agents. One engineer. Your entire infrastructure, covered.

The cloud operation runs on one specialist with three agents — each monitoring a different layer of your infrastructure continuously.

Agent · always on
Infrastructure Monitoring Agent
Starting skills — calibrated to your GCP environment in 60 days. New skills added as the operation learns your infrastructure.
GCP cost tracking Resource utilization IAM audit Billing anomaly detection Quota monitoring Service account hygiene
New skills added as the operation matures
→
Detects Quantifies Alerts
Human · always in charge
Cloud Engineer

Architecture decisions

GCP project structure, IAM policy design, and infrastructure choices that shape the environment.

Cost optimization

Interprets agent alerts, restructures queries, eliminates waste — with context on what's essential and what's not.

Access governance

Scopes permissions, disables stale accounts, documents policies — judgment the agent can't provide.

Agents · always on
Pipeline & Deployment Agents
Two agents — one watches data pipelines, one watches deployments. Both calibrated to your environment.
ETL/ELT health monitoring Data freshness validation Schema drift detection Scheduled job tracking Deployment health checks Config drift detection
New skills added as the operation matures
→
Monitors Validates Flags
Human · always in charge
Cloud Engineer

Pipeline design & repair

Builds, restructures, and fixes ETL/ELT pipelines — decisions that require understanding how your data moves.

Deployment management

Patches, updates, scales, and configures — with judgment on timing, risk, and downstream impact.

Incident resolution

When something breaks, the engineer diagnoses root cause, fixes it, and documents what changed — before your team notices.

02 · What it looks like

A week inside a running cloud operation.

The embedded team in action — every entry is a real output, not a capability description.

Operation log · Cloud Week of 14 Apr 2026
Monday
03:40
Infra Agent
BigQuery slot utilization at 89%. Cost projection exceeds monthly budget by ₹1.2L at current rate. via cost tracking
09:15
Cloud Eng.
Query audit completed. 3 unoptimized scheduled queries identified — partitioning fix reduces projected cost by 34%. Changes deployed.
Tuesday
06:00
Pipeline Agent
ETL pipeline for marketing attribution failed at transform step. Data freshness now −18hrs. via pipeline health monitoring
08:30
Cloud Eng.
Root cause: schema change in upstream CRM export. Transform updated. Backfill running. Marketing team notified — attribution data restored by EOD.
Wednesday
11:00
Infra Agent
IAM audit: 4 service accounts with owner-level permissions. 2 unused for 90+ days. via access audit
14:00
Cloud Eng.
Permissions scoped down to minimum required. Unused service accounts disabled. IAM policy documented and shared with client IT.
Thursday
02:30
Deploy Agent
Cloud Function v2.3 memory allocation at 94% during peak. Performance degradation imminent. via deployment health check
10:00
Cloud Eng.
Memory configuration optimized. Auto-scaling rules adjusted. Performance validated against last 7 days of traffic patterns.
Friday
11:00
Your Team
Weekly sync — 20 min. Cost reduction confirmed (₹1.2L/month saved). Pipeline fix validated. Next week: new Looker dashboard deployment.
This is what a permanently running cloud operation produces — every week, without being asked.
03 · The compounding effect

Infrastructure knowledge should never walk out the door.

When a cloud engineer leaves, they take the map of your entire environment with them. A permanent operation means the knowledge stays in the system.

Months 1–3

Mapping your environment

Infrastructure is documented — GCP projects, IAM policies, pipeline dependencies mapped for the first time
Cost baseline established — current spend itemized, optimization opportunities identified
Incidents are reactive — the team responds to issues as they surface
What this feels like
Visibility you didn't have before. You finally know what your infrastructure actually looks like.
Months 4–8

Patterns emerge

Cost optimization is proactive — recurring waste patterns identified and eliminated before they compound
Monitoring is tuned — false positives eliminated, alerts calibrated to your specific thresholds
Maintenance windows are predictable — patches and updates scheduled, not crisis-driven
What this feels like
Fewer surprises. Infrastructure is becoming predictable instead of fragile.
Months 9+

The environment runs itself

Capacity planning is evidence-based — months of operational data inform architecture and cost decisions
Agent skills cover your edge cases — monitoring built for the specific patterns in your environment
New deployments are faster — templates, runbooks, and patterns from your infrastructure make every addition smoother
What this feels like
Infrastructure you don't think about — because someone who knows it better than anyone on your team is watching it continuously.

A new cloud engineer starting fresh would spend months just figuring out what exists. A permanent operation is already watching, optimizing, and compounding — and every month it knows your environment better.

Your data infrastructure shouldn't break every time someone leaves.

30 minutes. We'll look at how your cloud infrastructure is currently managed, where the blind spots are, and whether a permanent operation makes sense.

Book a 30-min briefing →
Home→How we engage
Engagement model

Most analytics vendors deliver. Then leave.

Tatvic embeds specialists and agents into your environment and runs your analytics, optimization, and cloud as a continuous operation — one that compounds over months and years.

Book a 30-min briefing → Compare engagement shapes
Current engagement tenures
Auto · Motorcycles
Royal Enfield
6y 2m
BFSI
Bajaj Finance
5y 3m
Media · OTT
Sony LIV
5y 1m
Auto
TVS Motors
4y 8m
Travel
Cleartrip
3y 2m
Avg. tenure~4 years
Operating since2009
01 · Why it matters

A vendor engagement vs. a permanent operation.

Typical vendor

Deliver and exit

Scoped to a deliverable. Defined start, defined end, handoff, exit.
Knowledge leaves. The people who built it move on. Context evaporates.
Quality decays. Tracking breaks. Dashboards drift. Nobody's watching.
Tatvic embedded operation

Run and compound

Scoped to a function. Measurement, optimization, infrastructure. Continuous.
Knowledge accumulates. Named specialists stay. Agents build context every week.
Quality is maintained. Agents monitor. Specialists maintain. Drift is caught.

Whether you start with a migration, an audit, or a standing operation — the goal is the same: build a permanent capability that compounds.

→
Most engagements start with a specific need — a migration, a broken implementation, a dashboard rebuild. The engagement shapes below determine how that need is served and what happens after.
02 · Three shapes

Same operation. Shaped to how you work.

Same hubs, same specialists. What varies is who drives the day-to-day.

Analytics Support
You drive. We execute.
Reactive · task-based · portal-managed
"We have analytics leadership internally. We need execution capacity — not another strategy layer."

Your team raises tasks through the portal. Tatvic's specialist teams execute against concurrency limits. You keep control of priorities and the queue.

ExecutionShared teams
Who drivesYou
Agents—
ResponsePortal SLA
Hub accessAnalytics + add-ons
Managed Operations
We run the function. You decide.
Proactive + reactive · PM-led · agent-powered
"We want someone to run our analytics function — not just support it. We want to focus on decisions."

Everything in Analytics Support, plus a proactive layer. A dedicated PM triages agent output, surfaces insights before you ask, and drives expansion. Agents monitor continuously. Specialists validate daily.

ExecutionTeams + PM triage
Who drivesPM + agents
AgentsContinuous
ResponsePM · 2–4 hours
Hub accessAnalytics + add-ons
Dedicated Team
Your people. Our methodology.
Named FTEs · methodology-wrapped · embedded
"We need dedicated people in our team — not a shared pool. They need to work how we work."

Named analysts, engineers, and specialists dedicated to your account. Operating under Tatvic's delivery methodology and knowledge management. If someone leaves, we replace them — the capability stays.

ExecutionDedicated FTEs
Who drivesYou
AgentsScope-dependent
ResponseDedicated availability
Hub accessScoped per engagement

Most enterprises start with one shape and evolve. Analytics Support deepens into Managed Operations as trust builds. The operation adapts — you don't re-procure.

03 · How it starts

Every engagement begins the same way.

Regardless of shape, the first weeks follow the same arc — learn the environment, fix what's broken, then operate.

01
Weeks 1–4

Embed

Specialists get access to your environment. They audit what exists — instrumentation, schemas, data flows, reporting surfaces. The team maps your landscape before changing anything.

Outcome
A complete picture of what works, what's broken, what's missing.
02
Weeks 4–8

Stabilize

Fix what the audit revealed — broken events, orphaned tags, consent gaps, undocumented schemas. Remediation is systematic, documented, and designed to not need repeating.

Outcome
A clean, documented environment ready for continuous operation.
03
Week 8+

Operate

The operation is running. Specialists maintain and evolve the environment. Dashboards are live. Issues are caught and resolved before they become problems. Every month builds on the last.

Outcome
A permanent capability that improves every month.

Most enterprises feel the difference by week 6. By month 6, the operation knows more about your environment than any single person on your team.

04 · In practice

Engagements that don't end.

~4y
Average engagement tenure
200+
Enterprises served since 2009
150+
Experiments run for one client
"They created a custom solution that reduced CPL by 26% for predictive campaigns. Tatvic's expertise made a significant impact on our campaign efficiency."
JC
Jatin Chikara
VP Marketing, Royal Enfield

Start with what you need. The operation builds from there.

30 minutes. We'll look at how your analytics is currently run, where it's breaking, and which engagement shape makes sense.

Book a 30-min briefing →