AI Agents for Pharma & Biotech
FISTA Solutions builds pharma and biotech AI agents for regulated document work: literature and protocol summarization, safety narrative drafting, deviation triage support, submission document assembly, and site query responses — each traceable to source, reviewed by qualified staff, and validated for intended use.
- 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 pharma and biotech?
Pharma agents summarize literature and protocols with citations, draft safety case narratives from structured data, classify deviations and retrieve similar cases, assemble submission and report sections from approved content, and draft responses to routine site queries.
- 01
Literature and protocol agent
Produces structured summaries citing the exact source passage, accelerating review without replacing it.
Clinical - 02
Safety narrative agent
Drafts case narratives from structured safety data, with every field traceable to its source record.
Safety - 03
Deviation triage agent
Classifies deviations, retrieves similar historical cases, and proposes an investigation path for quality review.
Quality - 04
Document assembly agent
Assembles submission and report sections from approved content with version and approval status visible.
Regulatory - 05
Site query agent
Drafts responses to routine site questions from approved study documents, escalating protocol-affecting items.
Operations
Requirements
What guardrails do pharma and biotech agents need?
In GxP environments the guardrail is validation: the agent's intended use is documented, its outputs are traceable and reviewed by qualified staff, its behavior is evaluated before use, and changes to models or prompts go through assessed change control.
| Guardrail | Why it matters here | How FISTA implements it |
|---|---|---|
| Intended use | GxP systems must be validated for a defined purpose. | Documented intended use and limits, risk assessment, and validation protocols proportionate to GxP impact. |
| Traceability | Every regulated output must trace to source. | Citations to source records and passages, retained inputs, and reason-for-change captured on amendments. |
| Qualified review | Regulated content requires qualified human approval. | Review gates with reviewer identity and signature meaning captured, and no autonomous regulated output. |
| Change control | Model and prompt changes alter validated behavior. | Versioned prompts and models with impact assessment, regression evaluation, and documented approval before deployment. |
| Data protection | Subject and safety data are highly sensitive. | Pseudonymization, region-pinned processing, restricted access, and no external model training on subject data. |
Where AI fits
Which pharma and biotech workflow should you automate first?
Start with literature summarization or site query drafting. Both have low GxP impact, clear human review, and immediate time savings, which establishes the validation pattern before the agent goes near safety or submission content.
- 01
1. Start at low GxP impact
Literature review and internal summaries save time with minimal validation burden and clear human review.
- 02
2. Document intended use early
Writing intended use and limits first makes validation proportionate and avoids scope creep into regulated output.
- 03
3. Evaluate against qualified reviewers
Compare agent drafts to reviewer output on a labeled set so quality is evidence, not impression.
- 04
4. Move to safety and submission drafting
Only with validation executed and change control in place for models and prompts.
- 05
5. Maintain the evaluation
Re-run the evaluation on every model or prompt change, retaining evidence as part of the change record.
Cost and timeline
How much does an AI agent for pharma and biotech cost, and how long does it take?
Cost is driven by validation depth and the number of regulated systems touched; timeline by quality review rather than engineering. FISTA does not quote blind: the scoping call returns an agent design, a validation plan, and a phased estimate.
Validation is a visible, planned share of the budget rather than a surprise at the end. FISTA scopes intended use, protocols, execution, and traceability explicitly so your quality group reviews the plan before the build starts.
Risk-based classification keeps that cost proportionate. An internal literature assistant and a system drafting safety narratives deserve different rigor, and FISTA classifies components by GxP impact during discovery.
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 pharma and biotech agents?
FISTA builds life sciences agents with documented intended use, validation evidence produced alongside the build, and traceability to source on every output. Work is contracted through a US entity with full IP assignment.
Pharma & Biotech specifics
- Intended use, limits, and risk assessment are documented before the agent is built, so validation is proportionate.
- Every output cites source records, and qualified reviewers approve anything entering a regulated process.
- Model and prompt changes run through impact assessment and regression evaluation with retained evidence.
- Subject data is pseudonymized, region-pinned, and never used to train external models.
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 generative AI be used in a GxP process?
Where the system is validated for a documented intended use, outputs are traceable, and qualified humans review before anything enters a regulated record. FISTA documents intended use and limits first, then validates proportionately to GxP impact.
02Do you provide validation documentation?
Yes — intended use, risk assessment, specifications, protocols, executed evidence, and a traceability matrix, produced alongside the build rather than reconstructed for an audit.
03How do you handle model updates in a validated system?
As change control: impact assessment, regression evaluation against the retained test set, documented approval, and version history, so the validated state is always known and defensible.
04Can agents draft safety narratives?
Yes, from structured safety data with every field traceable to its source record, for qualified reviewer editing and approval. The agent removes assembly time; the reviewer remains accountable.
05Is subject data used to train models?
No. Deployments use enterprise endpoints with training disabled, or self-hosted models where residency requires it, with pseudonymization applied before any processing.
Scoped in writing before you commit
Draft faster inside a validated envelope.
Bring the document burden and your quality requirements. The scoping call returns an agent design, an intended-use statement, and a validation plan.