- Home
- AI Development Services
- AI Research Agent
AI Research Agent
Research that a person does well takes hours and does not scale. Research that an AI does badly is worse than none, because it is confidently wrong. The difference is entirely in citation discipline and source verification.
The trust problem, solved structurally
A research output is only useful if the reader can check it in under a minute.
We build research agents that cannot state a claim without attaching its source. Where two sources disagree, both are surfaced with the conflict flagged rather than silently resolved. Where a claim rests on a single unverified source, it is labelled as such.
Recency is enforced too: source dates are extracted and displayed, and a claim resting on a five-year-old page is marked rather than presented as current.
- Every claim carries a source link and date
- Conflicting sources surfaced, not silently resolved
- Confidence labelled per claim, not per document
- Single-source claims explicitly flagged
- Internal documents and external sources clearly distinguished
- Output to your template so it is comparable across runs
Research work it handles
Account and prospect briefs
Company profile, recent developments, stakeholders and likely priorities before a sales call.
Competitive monitoring
Track pricing, positioning, launches and hiring across a named competitor set on a schedule.
Market and category scans
Sizing, segmentation, vendor landscape and regulatory context with sourcing throughout.
Due diligence support
Structured evidence gathering against a checklist, with gaps explicitly reported as gaps.
Internal knowledge synthesis
Answer a question across contracts, reports and past project documentation with citations.
Regulatory and policy tracking
Monitor named sources for changes and summarise the practical implication.
Sources it reads
- Web search and page retrieval with date extraction
- Internal document stores: SharePoint, Drive, Confluence, S3
- CRM history and past correspondence
- Databases and data warehouses
- Subscription data providers via API where you hold a licence
- Output to Slack, email, Notion, Google Docs or your CRM
Ten stages from first call to a system your team trusts
Every AI engagement runs this sequence. Small projects compress stages; regulated projects expand them. Nothing gets skipped silently.
Discovery
A working session with your operations and engineering leads to map the process, the systems it touches, and where the cost actually sits.
AI Opportunity Assessment
We score candidate use cases on data readiness, volume, error tolerance and payback, then rank them. Some come back "do not use AI for this" — you get that answer too.
Solution Architecture
Model selection, retrieval design, tool boundaries, data flow, failure modes and hosting topology, documented before code.
Proof of Concept
A narrow build against your real data to prove accuracy on the cases that matter, typically 2–4 weeks. Go / no-go decision at the end.
MVP
One workflow, end to end, in the hands of real users. Evaluation sets and quality thresholds are defined here, not retrofitted.
Production Development
Hardening: error handling, retries, fallbacks, cost controls, rate limits, observability, and a human escalation path for every automated decision.
Integration
Wiring into your CRM, ERP, HRMS, data warehouse, ticketing and messaging channels through APIs, webhooks and event queues.
Security Testing
Prompt-injection testing, access-control verification, PII handling review, dependency scanning and penetration testing before go-live.
Deployment
Staged rollout on your cloud or ours, with CI/CD, versioned prompts and models, and rollback in place from day one.
Monitoring & Optimization
Quality dashboards, drift detection, cost-per-transaction tracking and a retraining or re-prompting cadence agreed in writing.
Security-conscious architecture, from the first design review
Enterprise AI fails on governance more often than on models. Every system we build is designed to support enterprise security requirements and to give your risk team answers rather than assurances.
Data privacy & residency
Your data stays in the region and tenancy you nominate. We architect for no-training-on-your-data configurations and document exactly which vendor endpoints see which fields.
Role-based access control
Retrieval and tool permissions inherit your existing roles. A user cannot surface a document through the AI that they could not open directly.
Authentication & authorization
SSO via OIDC/SAML, short-lived tokens for agent tool calls, and per-tool scopes so an agent holds the narrowest possible privilege.
Encryption
TLS in transit, AES-256 at rest, managed keys via your cloud KMS, and encrypted vector stores for embedded content.
API security
Gateway-level authentication, signed webhooks, IP allowlisting, request validation and quota enforcement on every exposed endpoint.
Audit logging
Every prompt, retrieval, tool call, model version and human override is logged with a trace ID, so any output can be reconstructed months later.
Data isolation
Per-tenant separation at the storage, index and key level for multi-entity groups and regulated environments.
Secure prompt handling
System instructions are server-side, user content is treated as untrusted input, and we test against prompt-injection and tool-abuse patterns.
PII protection
Detection, masking or tokenisation of personal data before it reaches a model, with configurable redaction policies per field.
Human approval workflows
High-impact actions — payments, refunds, contract sends, record deletion — route to a named approver instead of executing autonomously.
Monitoring & anomaly detection
Alerting on unusual tool usage, cost spikes, refusal rates and quality regressions.
Rate limiting & abuse control
Per-user and per-tenant throttles, spend caps and circuit breakers so a runaway loop cannot become a runaway invoice.
Secure deployment
Private networking, secrets in a managed vault, immutable builds, dependency scanning, and infrastructure as code.
Questions enterprise buyers ask us first
Can research output be trusted without checking?
How does it handle paywalled or licensed sources?
Can it run on a schedule?
How long does a research run take?
Related AI services
Talk to an AI solution architect
No junior sales rep, no discovery deck. The person on the call is the person who will design the system.
- Response within one business day
- Mutual NDA signed before detailed discussion
- Written scope, one price, one delivery date
- You own all source code, models and IP at launch
Bring us the process that is costing you the most.
Book a 45-minute call with a solution architect. You leave with a use-case shortlist, a reference architecture sketch and a realistic build envelope — whether or not you build it with Ezulix.