Dallas, Texas · direct or as your delivery partner

Enterprise AI agents, in production.

InsightNext puts engineers inside the systems you already run — deep fundamentals across AI, data, infrastructure and business analytics. The agent, the tables underneath it and the pipeline that ships it are one team's problem, not three vendors'.

Systems of record are reached through one audited path - an MCP toolbox, trusted queries, or a graph built by a pipeline - so the agent holds no credentials of its own. Observability and governance wrap the whole flow: traces, evaluations, screening and access narrowed at handover Systems of record the ones you run One audited path MCP toolbox, trusted queries, or a graph a pipeline builds Agent holds no credentials the agent never reads a source system directly OBSERVABILITY & GOVERNANCE traces, evaluations, prompt and response screening, access narrowed at handover

How we build, across every engagement. What sits in the middle changes — a toolbox of fixed tools, a catalogue of queries finance already trusts, a graph a pipeline writes. The rule, and what watches it, do not.

10 weeks

kickoff to go-live

Manufacturing · case study

100M

documents searchable

Media · case study

1,400+

users on one rollout

Software · case study

5 weeks

to foundation acceptance

Enterprise software · case study

Practices

What we do

One team across all of them. Most engagements start with a two-week assessment and grow from there.

Agents over enterprise data

Custom agents that answer real operational questions from SAP, finance warehouses and line-of-business systems — with guardrails, traceable numbers and no write-back until you say so.

Data foundations for AI readiness

An agent is only as good as the tables underneath it. We build the layer agents actually read from: curated views that encode the business rules once, extraction that keeps them current, and quality checks that fail loudly before an agent quotes a wrong number.

Search and platform engineering

Vertex AI Search at scale: multi-language data stores, indexing pipelines, intent-aware ranking and the observability to know when something is wrong before your users do.

Gemini Enterprise deployments

Pilot-to-production rollouts of Gemini Enterprise: identity (Okta, Google, Entra), connectors to the systems your teams live in, data-store design, and the enablement that gets people using it.

Infrastructure modernization

The platform work underneath the agents: landing zones that satisfy your auditors, honest capacity and cost modelling for accelerators, and delivery pipelines that let teams ship without anyone touching a console.

Contact-center and conversational AI

Voice and chat agents for regulated industries — healthcare, financial services, insurance — with retrieval grounded in your knowledge base and humans in the loop where it matters.

Citizen developer platforms

A governed path for business teams to ship their own applications, so enablement doesn't end with handing out project-owner roles. Reusable pipeline templates, an audited landing zone, and no manual console access anywhere in the flow.

More on each practice

Case studies

Selected work

Anonymised where the client prefers it. Ask us for references.

Apparel & retail · Commercial analytics

Gross-profit explainability over a knowledge graph

A weekly pipeline decomposes gross profit down its driver tree and attributes the variance in dollars. Deterministic math wherever a derivation exists; models only where one does not.

Read the case study

15 weeksto a reconciling driver tree

Enterprise software · Internal developer platforms

A citizen-developer platform with zero manual console access

A self-service CI/CD platform that lets internal business teams ship their own apps through a single governed pipeline — zero manual cloud-console access, four delivery phases, foundation phase accepted in five weeks.

Read the case study

5 weeksto foundation acceptance

Marketing technology · Creative platforms

Creative generation agents that never block on the model

An agent pipeline that generates brand-compliant marketing creative in the background, returns a session handle in under a second, and polls to completion instead of blocking the browser.

Read the case study

13 agentsacross nine session types

All case studies

Delivery

How an engagement runs

The same four stages whether it's a five-week pilot or a twenty-week platform build. Gates between each, with a named approver.

1

Discovery

Two to three weeks of workshops with the people who own the process. We leave with a written requirements document, a data-gap assessment, and an architecture you can approve.

2

Build

Foundation, security and governance first, then the agent. Every decision is logged with who made it and why, so sign-off is a formality rather than a surprise.

3

Pilot

A named group of real users on production data, a feedback sheet, and weekly tuning. We measure against agreed scenarios, not vibes.

4

Handover

Runbooks, knowledge transfer, IAM narrowed to what production needs, and our own access removed. You should not need us to keep it running.

Engagement models

Two ways to work with us

Directly.

You have a system with answers locked in it and people who need them. We scope, build and hand over.

As a delivery partner.

You are a cloud consultancy or digital agency with a signed statement of work and a capacity gap. We staff the engagement under your paper, mirror your deliverables, and stay invisible to your client if that's what you want. Most of our 2026 work runs this way.

Get in touch

Talk to us

Tell us what system the answer lives in and who needs it. We'll reply with a view on whether it's a two-week assessment, a five-week pilot, or something else.

Start a conversation →

or akash@insightnext.tech

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