Industries / Technology

Engineering teams shipping their own agentic features, faster.

Technology companies don't need another vendor pitching AI. They need peer-level engineering support to build, ship, and scale their own agentic and cloud-native products.

60+ engagements delivered Across the technology sector
100% of agent actions logged, replayable & reversible
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KEY CHALLENGES

Engineering backlogs outgrowing headcount
Roadmap ambition regularly outpaces hiring plans.
Platform decisions with long-term consequences
Architecture choices made under deadline pressure compound over years.
GenAI features that need to ship without breaking trust
Users notice hallucinations and inconsistent behaviour fast.
Scaling infrastructure ahead of growth curves
Under-provisioning during a growth spike is expensive to fix live.
QA and security debt building under release pressure
Shipping fast and shipping safely compete for the same hours.

WHERE WE START

SaaS

SaaS

Product engineering for multi-tenant platforms, from architecture through billing and platform scaling.

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AI solutions for Technology.

Where autonomous agents earn their keep in this sector, with an audit trail behind every action.

OUR TECHNOLOGY STACK TECHNOLOGY EXPERIENCE

AWS
Google Cloud
Kubernetes
Snowflake
OpenAI
60+ engagements delivered

Across the technology sector

Read the case study

Ten industries. One audited standard.

Different regulations, different systems of record — the same discipline every time: agents that run inside guardrails and leave a record you can read.

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Technology case studies

Challenge, solution, results. Four engagements in this sector.

Anonymised at the client's request. Each card states the pain point, what we built, and what changed. Plus the service type and country.

04 studies
AI MVPs USA

Support-ticket drafting MVP for a mid-market HR software vendor

Mid-market HR software vendor, Austin

Challenge
A support team of nine answered the same configuration questions repeatedly, but the knowledge base was out of date and agents rewrote answers from memory. First-response time had crept past eight hours, and new hires took three months to reach the team's average resolution rate.
Solution
In a six-week MVP we indexed the product documentation, release notes and two years of resolved tickets, then built a drafting assistant that proposes a reply with the sources it used cited inline. Agents edit and send; rejected drafts are reviewed weekly to fix gaps in the documentation itself.
Results
  • First-response time down from 8.2 hours to 2.5
  • 64% of drafts sent with only minor edits
  • New-hire ramp to average resolution rate shortened to 6 weeks
Service type · AI MVPsCountry · USA
Web UK

Developer documentation and licensing portal for a software firm

B2B software firm, Cambridge

Challenge
Customers downloaded licence files by emailing sales, documentation lived in a wiki that nobody owned, and renewals were tracked in a CRM that did not know which version each customer was running. Engineering fielded about 40 support emails a week that were really documentation questions.
Solution
We built a customer portal where licences are generated, renewed and revoked automatically against the CRM, and documentation is published from the product repository on every release through a CI pipeline. Usage of each page is measured so the team can see which topics generate support traffic.
Results
  • Licence-related sales emails reduced by roughly 85%
  • Documentation-related support emails down from 40 to 12 a week
  • Docs now publish within 10 minutes of each release tag
Service type · WebCountry · UK
AI Agents USA

Release-notes and changelog agent for a developer-tools company

Series B developer-tools company, Bay Area

Challenge
The company shipped several times a day, but release notes were written by a product manager on Friday afternoons from a scroll through merged pull requests. Customer-facing notes regularly missed breaking changes, and the changelog was a week behind what was actually in production.
Solution
We built an agent that watches merged pull requests, groups them by feature area, drafts customer-facing notes in the company's style guide, and flags anything touching public APIs as a potential breaking change. A PM approves each draft in Slack before publication; every approval and edit is retained.
Results
  • Changelog published within 2 hours of each release
  • Breaking changes missed in notes down from 6 to 0 per quarter
  • Weekly release-notes effort cut from 4 hours to 30 minutes
Service type · AI AgentsCountry · USA
Apps USA

Azure cost-governance app for a managed-service provider

Mid-market managed-service provider, Colorado

Challenge
The provider managed Azure estates for around 60 clients and produced monthly cost reports by exporting billing data into spreadsheets. Untagged resources were allocated by guesswork, idle virtual machines ran for months unnoticed, and two clients disputed invoices in the same quarter.
Solution
We built an internal app that pulls billing and resource data nightly, enforces the provider's tagging policy with a weekly exceptions list, and produces per-client cost reports automatically. Right-sizing and idle-resource recommendations are queued for an engineer to accept or dismiss, with the decision recorded.
Results
  • Untagged spend reduced from 23% of total to under 3%
  • Client cost reports produced in minutes rather than 3 days
  • Roughly $210k a year in idle resources identified and removed
Service type · AppsCountry · USA

FAQ

Technology + agentic AI: questions, answered.

How can AI agents help software companies increase engineering throughput?

AGS agents take on routine build, test, and triage work so engineers stay on the hard problems as backlogs outgrow headcount. Agents run inside CI/CD and observability tooling with a full audit trail, the level of scrutiny an engineering-led buyer demands. Work moves faster without trading away the visibility your team relies on to trust automation.

Can autonomous agents automate technical support at scale?

Yes. AGS agents resolve common support tickets autonomously and hand off the rest cleanly, giving software companies 24/7 support and ops coverage. Retrieval-grounded so responses hold up in production, agents log every action and run under least-privilege identity. Users get fast answers without the hallucinations and inconsistency that erode trust in AI support.

How does AGS keep GenAI features reliable in production?

AGS builds retrieval-grounded features that are evaluated systematically as models change, so behaviour holds up rather than drifting. Agents operate with observability and an audit trail an engineering buyer can inspect. Every action is logged and reversible, and human-in-the-loop gates cover the decisions that matter, so you ship agentic features without breaking user trust.

What RevOps and data workflows can AGS agents automate for tech companies?

AGS deploys data-grounded agents across the funnel, from enrichment and lead routing to renewal and expansion. Agents work off governed data on your platform, act autonomously on routine steps, and log every change for clean attribution. Least-privilege access and reversibility mean revenue operations speed up without loosening control over customer data.

Is agentic AI safe enough for engineering-led teams to adopt?

Yes. AGS was built for buyers who expect autonomy to survive scrutiny. Agents run under least-privilege identity, every action is logged and reversible, and human-in-the-loop gates guard consequential steps. Our controls are aligned to ISO 27001 and SOC 2, and agents ship with the observability and audit trail an engineering organisation demands before trusting production automation.

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