AI Agents for Marketing
FISTA Solutions builds marketing AI agents that raise output without lowering standards: drafting content within brand and claim rules, writing reporting narratives from warehouse data with linked figures, checking campaigns before launch, synthesizing research into themes, and preparing client reports.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What can AI agents do in marketing?
Marketing agents draft channel variants within brand voice and claim rules, write the explanation behind reporting numbers with each figure linked to its query, check tracking and targeting before launch, synthesize research into themes with quotes, and assemble client reports for review.
- 01
Content production agent
Drafts channel variants within brand voice and claim rules, with human approval before publication.
Content - 02
Reporting narrative agent
Writes what changed and why from warehouse data, with every figure linked to its query.
Reporting - 03
Campaign QA agent
Checks tracking parameters, links, targeting, and suppression before launch, catching spend-wasting errors.
Operations - 04
Research synthesis agent
Turns research, reviews, and tickets into themes with example quotes attached.
Insight - 05
Client reporting agent
Assembles agency client reports from platform and warehouse data for account team review.
Agency
Requirements
What guardrails do marketing agents need?
Marketing agents generate material that will be published and numbers that will be quoted, so guardrails cover claim safety, metric integrity, and consent: claims are rule-checked, figures come from defined warehouse metrics, and suppression rules are honored everywhere.
| Guardrail | Why it matters here | How FISTA implements it |
|---|---|---|
| Claim safety | Generated copy can invent facts and unsupported claims. | Prohibited-claim lists, grounding in approved product data, and mandatory human approval before publication. |
| Metric integrity | Reported numbers must match the warehouse. | Figures produced from defined warehouse metrics with the query linked, never estimated by the model. |
| Consent and suppression | Outbound must honor preferences. | Suppression enforced at activation, with the agent unable to bypass preference and consent rules. |
| Brand consistency | Volume without voice damages the brand. | Brand rules encoded and evaluated per output, with sampling review to catch drift over time. |
| Attribution honesty | Marketing claims of impact face scrutiny. | Narratives state what the data supports and flag where causality is not established. |
Where AI fits
Which marketing workflow should you automate first?
Start with reporting narratives. The data already exists, the output is internal, and it removes hours of recurring writing while proving whether your metric definitions are solid enough to automate against.
- 01
1. Automate reporting narratives
Internal, recurring, and it exposes weak metric definitions as a useful side effect.
- 02
2. QA campaigns before launch
Tracking and targeting checks prevent wasted spend with no publication risk.
- 03
3. Synthesize research
Themes with quotes from research, reviews, and tickets, reviewed by the team.
- 04
4. Draft content at volume
Only with brand and claim rules encoded and approval enforced before publication.
- 05
5. Automate client reporting
Agency reports assembled from data, with account teams reviewing before delivery.
Cost and timeline
How much does an AI agent for marketing cost, and how long does it take?
Cost is driven by data readiness and content governance; timeline by warehouse metric definitions and brand rule capture. FISTA does not quote blind: the scoping call returns an agent design and a phased estimate.
Metric definitions come first for anything reporting-related. Narratives built on inconsistent definitions produce confident, wrong explanations, so FISTA audits the metric layer before automating commentary on it.
Brand rule capture is the equivalent for content. Writing down voice, claim boundaries, and prohibited language is work your team must do once, and it is what makes generated volume safe rather than risky.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI agent into production?
FISTA delivers agents in four gated phases: a discovery sprint that picks the workflow and writes the agent specification, a design that names tools, permissions, and approval points, a build with an evaluation harness and shadow runs on real work, and a production release with traces, dashboards, and rollback.
- 1
Select and specify
Choose the workflow with a measurable outcome, map its systems and edge cases, and write the agent spec with success metrics.
OutputAgent specification, golden test set
- 2
Design the guardrails
Tool inventory with least-privilege scopes, approval gates, escalation paths, data handling, and the evaluation plan.
OutputTool and permission matrix
- 3
Build and shadow-run
Implement tools as MCP servers or connectors, iterate against the evaluation harness, and run in shadow mode on live inputs.
OutputShadow-mode results, eval scores
- 4
Release and observe
Graduated rollout, full traces, cost and quality dashboards, on-call runbook, and a change process that re-runs the evals.
OutputProduction agent with SLOs
Why FISTA
Why choose FISTA Solutions to build your marketing agents?
FISTA builds marketing agents that cannot publish without approval, cannot invent a figure, and cannot bypass suppression. Work is contracted through a US entity with full IP assignment.
Marketing specifics
- Every figure in a narrative links to the warehouse query that produced it; the model never estimates numbers.
- Prohibited claims and brand rules are enforced in the pipeline, with approval required before publication.
- Suppression and consent are enforced at activation, and the agent has no path around them.
- Narratives state what the data supports and flag where causality is unproven.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What teams ask before deploying agents.
Straightforward guidance for evaluating scope, fit, and the next step.
01Can AI write our marketing content at scale?
Yes, within brand voice and prohibited-claim rules, grounded in approved product data, with human approval before publication and provenance recorded. Unreviewed generated content is how brands publish claims they cannot support.
02Will reporting narratives be accurate?
Figures come from defined warehouse metrics with the query linked, so they match your source of truth. The narrative explains movement and flags where causality is not established rather than asserting it.
03Can an agent send campaigns?
It can prepare them, run pre-launch QA, and stage them for approval. Sending is gated by human approval and by suppression and consent enforcement that the agent cannot bypass.
04Do we need a warehouse first?
For reporting agents, effectively yes — a defined metric layer is what makes automated commentary trustworthy. Content and QA agents can start earlier.
05How long until it is useful?
Reporting and QA agents typically go live within weeks where the data layer is sound. Content agents follow brand rule capture, which is usually a short but necessary exercise.
Scoped in writing before you commit
Produce more without publishing something you regret.
Bring the reporting load or the content backlog. The scoping call returns an agent design, a governance plan, and a phased estimate.