Gravity 9 Solutions

Gravity 9 Solutions We empower organizations by integrating human expertise with autonomous technology.

By bridging this gap, we help organizations streamline operations, enhancing decision-making, and driving transformational growth - turning potential into progress.

A feature flag platform doesn't implement itself. Most teams license LaunchDarkly, wire up a few flags, and stop there. ...
02/09/2026

A feature flag platform doesn't implement itself. Most teams license LaunchDarkly, wire up a few flags, and stop there. The value sits in flag strategy and architecture, not the SDK install.
gravity9 works as a LaunchDarkly implementation partner across the full rollout, not just the tooling:
Feature flag strategy and architecture, so flags map to actual release risk instead of getting bolted onto whatever ships next
Progressive rollout implementation, canary deployments and percentage-based rollouts built into the delivery pipeline
Testing strategy for flag-heavy codebases, where indeterminism from overlapping flags is the real defect risk, not the flags themselves
Release automation tied to the flag layer, so rollback is a toggle, not a redeploy
That's the gap between having LaunchDarkly and getting value from it: the platform is rarely the blocker, the implementation around it is.
Find out more: https://hubs.la/Q04wcvwy0

01/09/2026

A lot of consultancies can talk about the tech. Fewer take the time to understand what the business actually needs from it, and why. That gap is where most engagements go wrong before a single line of code gets written.
We'd rather start with the business problem and let the technology follow.

31/08/2026

The platforms that fit.
We're partners with all three major cloud infrastructure providers: AWS, Azure, and Google Cloud. Not because we collect badges, but because our clients need the platform that actually fits their environment.
An enterprise already running on Microsoft's stack gets a different recommendation than one that's cloud-native and data-heavy. We stay platform-agnostic so that decision is driven by the client's architecture, not by which vendor we're pushing.
See the full partner list at https://hubs.la/Q04vR4vz0.

GitHub has published a useful article of how its engineering team evaluates LLMs for production secret scanning.The inte...
28/08/2026

GitHub has published a useful article of how its engineering team evaluates LLMs for production secret scanning.
The interesting part is how they approached evaluation not the secret scanning use case.
Before tuning prompts or comparing models, they started with the product decision: what outcome matters, which failures are acceptable, and what safety and operational guardrails need to hold.

That is an important distinction. In production AI systems, a better model score doesn't necessarily mean a better system. Inputs are messy, context changes, labels can be unreliable, and different types of errors carry very different consequences.
GitHub also treats offline evaluation much like integration testing, rerunning evaluations as prompts, models, context and system configuration change. We think this should become standard practice for agentic systems.
An evaluation suite should be part of the delivery lifecycle and, where practical, the CI/CD pipeline. Changes to a model, prompt, tool, skill or context shouldn't reach production without understanding what behaviour changed and whether critical guardrails still hold.

This is also part of AI readiness that can easily be overlooked. Before asking whether an organisation is ready to build with AI, we should also ask:
Are you ready to define what success looks like, what the system is allowed to get wrong, and how you'll know when its behaviour changes?
That's a much more useful starting point than choosing the model first.

Read more about gravity9's approach to AI Readiness:
https://hubs.la/Q04vDLB70
GitHub: How to evaluate LLMs before production (https://hubs.la/Q04vF6G70)

Julio Castellanos, Principal Consultant - Solution Architect at gravity9, attended Latam Architecture Day 2026 at the Gr...
25/08/2026

Julio Castellanos, Principal Consultant - Solution Architect at gravity9, attended Latam Architecture Day 2026 at the Grand Hyatt Bogotá. Congratulations to PRAGMA and Universidad de los Andes (Colombia) for bringing together hundreds of architects from more than 10 countries for a full day on the present and future of the architect's role in the new world of AI.
The lineup was stacked. Chris Richardson (yes, the Microservices Patterns author) spoke on architecting for fast flow. Rick Kazman from Carnegie Mellon broke down how humans and AI can co-create software design, with a line that stuck: creativity isn't just novelty, it's novelty plus revelation. And Nequi's own CEO talked architecture all the way from the boardroom down to the code.
It was a big impact, seeing how other companies think and develop their own AI strategies, and a good chance to step outside the day-to-day. Some of it is very relevant to what we do at gravity9: architecture and orchestration, AI-assisted design, agentic systems in production, fast flow for delivery teams.
A great day of learning, and a good reminder that as our tools evolve, the fundamentals of architecture remain as relevant as ever.

A single influencer campaign brief used to take a month to turn into a shortlist. Not because the team was slow, but bec...
24/08/2026

A single influencer campaign brief used to take a month to turn into a shortlist. Not because the team was slow, but because the process was entirely manual and relationship-driven, with no way to scale it past headcount.
We built an AI matching engine on MongoDB Atlas to fix that. It scores every recommendation against nearly 9,000 real past hiring outcomes, so campaign managers get a ranked shortlist with a plain-language explanation for every pick, not a black box.
The engine correctly recovers 67.88% of influencers actually hired on campaigns it had never seen before. Shortlisting time dropped from a month to minutes.
Read the full case study to see the five-layer system behind it, from vector search through to the learned ranking model.
Read the case study: https://hubs.ly/Q04tYRDf0

21/08/2026

For Marta Rydel, working across different countries and time zones doesn't feel like a coordination problem. It feels like this: "I'm surrounded by a team of incredibly friendly, supportive professionals who make even remote collaboration feel seamless. Despite our different locations, our relationship makes every day enriching…"
That's the kind of team we've built on purpose. Senior people who know what they're doing, spread across offices on three continents, working together in a way that doesn't feel like it should work as well as it does.
If you want to find out what that's actually like, we're hiring. Take a look at what's open: https://hubs.ly/Q04t956F0

20/08/2026

An adverse event system that only stores data is doing half its job.
These systems exist to record when something goes wrong, a medical device failing, a patient harmed. But capturing the record was never the hard part. Understanding it quickly enough to act is where most systems fall short, because the data that would tell you what's actually happening sits in pieces: structured records in one place, unstructured reports in another, workflows that don't talk to each other.

That fragmentation is the real problem, not the storage.
MongoDB Atlas Vector Search changes what's possible here. Instead of keyword matching across disconnected tables, teams can search by meaning, connect structured and unstructured data in one place, and spot patterns that would otherwise take weeks to surface manually. That's the difference between a system that stores records and one that produces insight.

We build this kind of modernisation for clients who can't afford to get it wrong.
Talk to gravity9 about modernising your data platforms with MongoDB: https://hubs.la/Q04tG6lp0

19/08/2026

Every business leader we talk to is asking the same question: how do we do more with less and become more efficient.
Andy Ross, Partner at gravity9, gives the same answer every time. Stop treating AI like a shiny new tool. Treat it like a partner that helps your people work smarter, faster, and with less friction.
We've already put that into practice. We cut clinician report writing from 72 hours down to 1. We've helped organisations make faster, better procurement decisions. We've given teams real-time access to supply chain information they used to wait days for.
None of that is about replacing people. It is about freeing up the time and judgement that was going into admin, so people can focus on the outcomes that actually matter to customers.
Andy's view: the organisations that act now, and build AI into how they operate rather than bolting it on, are the ones still setting the pace in five years.
Read more from Andy: https://hubs.la/Q04t99680

18/08/2026

We run the same AI-enabled delivery practices internally that we help our clients adopt. Not as a talking point, as the standard for how our own teams build, test, and ship.
That is why we can tell clients exactly what AI-assisted delivery changes and what it doesn't, because we measure it against the same commercial numbers that show up in their board packs, not our own activity metrics.
It shows up in the results. An NPS of +88, more than double what the industry considers strong. Two consecutive years on the Sunday Times Top 100 Fastest Growing Companies list. Named MongoDB's Global Implementation Partner for 2025.
We've delivered inside Barclays, HSBC, Allianz, Ford, IKEA, and Sainsbury's, organisations with no tolerance for vague delivery.
Building the future together. At pace.
https://hubs.la/Q04tk6b50

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