Research AI Agent Development
FISTA Solutions builds research AI agents that produce work an analyst can defend: gathering from approved internal and external sources, verifying claims across sources, synthesizing into structured briefs with citations and dates, and stating uncertainty instead of smoothing it over.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does a research AI agent do?
Research agents plan a search strategy, gather from allowlisted sources, cross-check claims, synthesize into a structured brief with citations and recency, flag contradictions and gaps, and deliver on a schedule or on demand into the tools the team already uses.
- 01
Structured briefs
Research output in a consistent structure with citations, dates, and confidence per section.
Output - 02
Source allowlisting
Gathering restricted to licensed and approved sources, so provenance is defensible.
Governance - 03
Cross-source verification
Claims checked across independent sources, with disagreement surfaced rather than averaged away.
Quality - 04
Monitoring digests
Scheduled digests on topics, competitors, or entities with change highlighted since last run.
Monitoring - 05
Internal corpus research
Research across your own documents and data with the same citation discipline as external work.
Internal
Requirements
What guardrails does a research agent need?
Research agents produce material people act on, so guardrails cover provenance, recency, and honesty: every claim carries a citation and date, sources are allowlisted, contradictions are surfaced, and the agent states what it could not establish.
| Guardrail | Why it matters | How FISTA implements it |
|---|---|---|
| Citation and date | Undated research is unusable and dangerous. | Every claim cites its source with publication or retrieval date, and staleness is flagged explicitly. |
| Source governance | Licensing and quality vary enormously. | Allowlisted sources with licence terms respected, and source tier shown so readers weigh evidence appropriately. |
| Contradiction handling | Averaging away disagreement hides risk. | Conflicting claims presented side by side with sources, rather than resolved silently by the model. |
| Uncertainty | Confident prose about weak evidence misleads. | Explicit confidence per section and a stated list of what could not be established. |
| Reproducibility | Analysts must be able to retrace the work. | Search plan, sources consulted, and prompt and model versions retained with every report. |
Where AI fits
Where should a research agent start?
Start with a recurring research task your team already does manually and to a known standard. The existing output becomes the quality benchmark, which makes the agent's contribution measurable rather than impressionistic.
- 01
1. Pick a recurring task
Weekly competitor briefs or standard diligence packs have an existing quality bar to measure against.
- 02
2. Allowlist the sources
Define what may be used, including licensed data, so provenance is defensible from the start.
- 03
3. Fix the output structure
A consistent template makes review fast and comparison across periods meaningful.
- 04
4. Compare against analyst output
Run in parallel and have analysts score coverage, accuracy, and usefulness.
- 05
5. Automate the schedule
Once quality holds, move to scheduled digests with change highlighting.
Cost and timeline
How much does a research agent cost, and how long does it take?
Cost is driven by source access and verification depth; timeline by licensing and corpus access. FISTA does not quote blind: the scoping call returns an agent design, a source plan, and a phased estimate.
Source licensing is often the gating item. Agents that respect licence terms need proper access to the data your team already pays for, and that provisioning takes longer than the engineering.
Verification depth is the quality dial and the cost dial. Cross-checking every claim across independent sources costs more per report and is worth it for diligence work; lighter verification suits monitoring digests.
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 build your research agent with FISTA Solutions?
FISTA builds research agents that cite and date every claim, respect source licensing, and admit what they could not establish. Work is contracted through a US entity with full IP assignment.
Research Agents specifics
- Every claim carries a citation and date, with staleness flagged rather than hidden.
- Sources are allowlisted and licence terms respected, so output is defensible in a client or board setting.
- Contradictions between sources are surfaced side by side instead of silently resolved.
- Each report retains its search plan, sources consulted, and model version, so the work can be retraced.
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 a research agent replace an analyst?
It removes gathering and first-draft synthesis, which is usually most of the hours, while analysts keep judgment, framing, and the recommendation. Output quality is measured against your analysts' existing work rather than asserted.
02How do you prevent fabricated citations?
Claims are generated only from retrieved content, citations are verified to resolve to real sources, and unverifiable statements are dropped or flagged. Fabricated references are a known failure mode and are checked for explicitly.
03Can it use our licensed data sources?
Yes, where your licence permits programmatic access, which is the usual gating item. FISTA respects licence terms in the source allowlist rather than scraping around them.
04How current is the research?
Every claim carries a retrieval or publication date, and monitoring digests highlight what changed since the previous run, so recency is visible rather than assumed.
05How long until it produces usable briefs?
A defined recurring task with accessible sources typically produces reviewable output within weeks, followed by tuning against analyst scoring.
Scoped in writing before you commit
Get the brief before the meeting, with sources attached.
Bring a recurring research task and your sources. The scoping call returns an agent design, a source plan, and a phased estimate.