Virtual Research Assistant Development

For open-web research, you shouldn't - those tools are excellent and cost roughly $20 a month. The case for building starts where they structurally stop: your private knowledge. Consumer research agents will never legally touch your case files, patient records, unreleased materials, or twenty-year internal archive, and they have no concept of who in your organization may read what. A custom agent works across that material, inside your permissions model, within your regulatory perimeter. You're not paying us to rebuild ChatGPT - you're paying for access to the knowledge ChatGPT can't have.

It means the agent retrieves only from documents the specific person asking is entitled to see, enforced at the index rather than filtered after generation. This matters because the alternative is a breach: an agent that summarizes a compensation file for a junior analyst has leaked it, even if it never showed the document. We inherit entitlements from your existing identity provider - Okta, Entra ID, SharePoint - so access stays correct when someone changes role. It's the single requirement that eliminates most off-the-shelf options for regulated organizations.

On the GAIA benchmark for general AI assistants, top agents reach roughly 75% accuracy in 2026 - a dramatic improvement from about 15% for GPT-4 with plugins in 2023. It's also a 25% failure rate, and we'd rather you hear that from us than discover it. OpenAI targets a "research intern" capability, and the word is chosen deliberately: interns are genuinely useful, and their work gets checked. We design the checking in - verification layers, calibrated uncertainty, and citation integrity - rather than pretending the failure rate is zero.

Several layers, because no single one suffices. We ground every claim to a specific retrieved passage rather than a document-level gesture. We verify that cited sources actually exist and are current. We check that the passage genuinely supports the claim being made. We surface contradictions between sources instead of silently choosing one. And we tune for calibrated uncertainty - saying "the sources don't clearly answer this" instead of producing something plausible. Research indicates models use more confident language when they're wrong, which is precisely why verification must be architectural rather than advisory.

Model Context Protocol has become the standard interface for exposing private data to AI agents. Rather than building a bespoke integration for every agent and every system, you build an MCP server for your internal database, archive, or document store, and any compatible agent can reach it. It's how organizations are constructing private knowledge networks. We build those servers - which means your internal knowledge becomes available to whichever agent layer you use now and whichever one replaces it later, without redoing the work.

Possibly, and we'll say so if that's the honest answer. Enterprise search platforms are strong at broad connectivity and permissions across many applications. Building makes sense when your domain workflow is genuinely specific, your data can't leave your infrastructure, your sources are unusual, or you need research embedded inside your own product. The market compares governance, integrations, and deployment model rather than answer quality - so the question isn't which is better, it's which fits your constraints. Our discovery phase exists partly to answer this.

Yes, and this is usually the real work. Most enterprise knowledge isn't in a database - it's in PDFs, scanned contracts, spreadsheets, and folders nobody has indexed since 2018. We handle extraction, OCR, chunking, and structuring, then index it with permissions attached. Data quality is the most common blocker on projects like these, which is why we audit sources before quoting. Occasionally the honest first project is cleaning and indexing your archive, not building an agent on top of a mess.

Cost tracks source count and messiness, permissions complexity, verification depth, integration count, and deployment model. A knowledge assistant over one clean archive is a different build from a permissions-aware layer spanning six systems with on-premise hosting and regulatory review. Ongoing costs are model usage plus infrastructure - and research agents consume more than chatbots, because multi-step retrieval loops run many queries per question. We scope fixed pricing after auditing your sources, since quoting before seeing your data is guesswork.

No, and the benchmark data says so plainly. At roughly 75% accuracy, an unsupervised research agent is a liability in any context where conclusions carry consequence. What it does is remove the retrieval slog - the hours spent finding, opening, and skimming - so your researchers arrive at synthesis and judgment faster. Your analysts stop being search engines and start being analysts. Anyone selling you analyst replacement in 2026 is describing a 2028 roadmap item as a present-day product.

Security is designed during architecture. Encryption in transit and at rest, permissions enforced at the index, full query and retrieval audit logging, and enterprise API tiers configured so your data isn't used for training. Where material legally cannot leave your perimeter, we deploy in your VPC, on-premise, or self-hosted with open models. As an ISO/IEC 27001:2022 certified organization we operate a formally audited ISMS. Research agents touch your most sensitive material by definition - case files, patient data, deal documents - so the deployment question usually comes before the capability question.