AI SOLUTION

Enterprise copilots that act, not just chat

We ship copilots grounded in your data, secured by your access model, and integrated with the tools your teams already use — Salesforce, Slack, Notion, ServiceNow, and internal systems.

CAPABILITIES

A copilot with production-grade wiring

Every copilot ships with the plumbing enterprise teams need on day one — auth, retrieval, actions, telemetry, and evaluation.

Grounded on internal data

Retrieval over your wikis, tickets, contracts, and databases — with citations back to the source document and version.

Role-based access

The copilot inherits your identity provider and existing group permissions, so answers respect what each user is allowed to see.

Structured actions & write-backs

Update a CRM record, open a ticket, post to Slack, or trigger a workflow — with schema validation and audit logs.

Streaming responses

Token streaming for perceived speed, with partial answers rendered as the model reasons and calls tools.

Feedback capture

Thumbs up and down, comment fields, and reviewer queues feed the evaluation set that keeps quality trending up.

Cost telemetry

Per-team token spend, cache hit rates, and tool-call cost dashboards — so finance can forecast, not just react.

HOW WE BUILD

From workflow to measured adoption

A four-phase engagement that gets the first useful copilot in front of users in weeks and expands from there.

01

Workflow discovery

We shadow the target team, map the questions they actually ask, and identify where a copilot is worth the effort versus where a form or a query is fine.

02

Data & permissions audit

Inventory of sources, freshness, sensitivity, and access rules. We document what the copilot can see, per role, before any code ships.

03

Copilot build & eval

Retrieval, tools, and prompts assembled behind a versioned evaluation set. We refuse to ship a version that regresses answer quality.

04

Rollout & adoption

Staged release with training, feedback capture, and a weekly review of usage, satisfaction, and cost until adoption is steady.

TECHNOLOGY

Copilot toolchain

Model providers, orchestration, enterprise integrations, and the observability layer that keeps the copilot healthy in production.

OpenAI Anthropic LangChain LlamaIndex Salesforce API Slack API Microsoft Graph Snowflake Postgres Okta Auth0 Datadog Terraform AWS
USE CASES

Copilots teams ship first

Departments where a grounded copilot pays back within a quarter.

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Sales enablement copilot

Answers about pricing, competitors, and product edge cases, pulled from your decks, battle cards, and closed-won notes.

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Customer support triage

Drafts first-touch replies from your macros, knowledge base, and past resolved tickets — with escalation rules built in.

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HR policy assistant

Consistent answers on leave, benefits, and expense policy that respect regional variations and current handbook versions.

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Finance close copilot

Walks accountants through reconciliations, variance explanations, and journal entry drafts against your GL and prior periods.

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Ops runbook helper

Guides on-call engineers and operators through documented procedures, with links to the exact runbook step and owner.

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Engineering incident copilot

Correlates alerts, past postmortems, and recent deploys to suggest likely causes and the fastest known mitigation.

BUSINESS IMPACT

What good copilot programs deliver

Concrete outcomes we design toward — and instrument from the first day of rollout.

01

Reclaim hours per user per week

Answers that used to require a Slack ping, a search, and a policy scan come back in seconds — with the citation attached.

02

Faster onboarding for new hires

A copilot that already knows your systems shortens the ramp for new joiners and reduces the load on tenured teammates.

03

Consistent policy answers

One source of truth for benefits, expense, security, and procurement questions — versioned and traceable.

04

Adoption you can measure

Weekly active users, deflection rates, and satisfaction scores wired to dashboards leadership can trust.

FEATURED WORK

A copilot grounded in the docs

NeuraDesk shows how a retrieval-augmented copilot answers with source citations users can verify.

NeuraDesk case study AI • RAG

NeuraDesk

A retrieval-augmented support copilot that grounds answers in a company's own knowledge base.

LangChain • RAG Pipeline • Vector Search • Evaluation

Read Case Study →
WHY ZIKOSOFT

Why teams choose us for enterprise copilots

A pragmatic partner for copilots that need to work inside a real access model — not a demo behind a login.

Learn more about us →
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Rooted in enterprise access

We design around your SSO, groups, and data-classification rules from the first sprint — not as a retrofit later.

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Ship in weeks, not quarters

A scoped pilot in six to ten weeks, then a controlled rollout — with each phase gated on evaluation results.

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Evaluation-driven quality

Golden question sets, regression tests, and reviewer queues keep answer quality steady as prompts and models change.

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Ownable, portable stack

No opaque platform lock-in. You keep the prompts, the retrieval pipeline, and the option to swap providers.

FAQ

Copilot questions we get

Answers to the questions security, IT, and product leaders raise before a copilot goes live.

How do you keep the copilot within a user's allowed data?
Every retrieval and tool call runs under the requesting user's identity. We index with access metadata and filter results at query time against your identity provider's group membership.
Where does our data go? Can we keep it in a specific region?
We can deploy against models hosted in your cloud region — Azure OpenAI, AWS Bedrock, or Google Vertex — or against Anthropic and OpenAI direct with signed data-processing agreements. No training on your prompts.
How do you drive adoption once the copilot ships?
We embed the copilot where the work already happens, seed it with a set of high-value questions per team, and run weekly office hours in the first month to close feedback loops fast.
What kind of latency should users expect?
For grounded single-turn answers, first tokens usually stream within one to two seconds and complete within four to eight. Agentic actions with tool calls are longer — we show progress states.
How do you decide which model to use?
We evaluate two or three candidates on your actual question set — measuring answer quality, latency, and cost. The result is documented and revisited quarterly as new models ship.
How predictable is the running cost?
Per-team token budgets, prompt caching, and a routing layer that sends easy questions to smaller models keep monthly cost inside a forecast we agree on before rollout.

Bring a copilot to a team that will actually use it.

Tell us the workflow and the systems it lives in. We'll come back with a scoped pilot, a data-access plan, and a target evaluation score.

Book a Copilot Discovery →
Building with AI? Zikosoft ships production-grade agentic systems with governance built in. Talk to our AI team →