From working demo to production software.
Embedded engineers who build your product, instrument the AI inside it, and harden both. Your repo and your cloud from day one.
OUR APPROACH
What we build: new products, rescues and AI systems
Greenfield or brownfield. Your stack, your cloud, your constraints. You own what ships from Day 1, and the engineers stay on Slack after launch.
GREENFIELD
Build AI-Native From Scratch
SaaS scoped, designed, and shipped with auth, multi-tenancy, and AI designed into the architecture — not bolted on after launch.
LEARN MORE →BROWNFIELD
Rescue, Rebuild & Modernize
Broken prototype, legacy codebase, or a no-code build at its ceiling. We audit first: what holds, what goes, what the rebuild costs.
LEARN MORE →AI SYSTEMS
LLM Pipelines, Agents & Evals
Prompt orchestration, agentic workflows, and retrieval — built with the eval harnesses and Langfuse tracing that catch model drift before a customer does.
LEARN MORE →THE PROBLEM
AI made the first version cheap. The rest of the work stayed expensive.
Five places the cost shows up.
01
The demo behaves. Production behaves differently.
Clean output in testing meets concurrent load, messy real-world input, and data that shifts week to week. Those failures surface in support tickets, not test runs.
02
The answer was wrong and the trail is cold.
Debugging a wrong answer takes the prompt chain, the retrieval step, and the model response that produced it. Without tracing, you start from nothing.
03
Output went up. Review capacity did not.
AI writes code faster than teams can review it. Across 470 open-source pull requests, CodeRabbit found AI-co-authored code carried ~1.7× more major issues and ~2.7× more security defects.
04
The feature shipped. The architecture stayed the same.
A model call bolted onto an existing request path holds until the product depends on it. Then rate limits, user-facing latency, and token cost all land in the same week.
05
The build landed. The reasoning left with the team.
Model choice, prompt structure, retrieval tuning — that context rarely reaches the repo. The code transfers cleanly. The judgment behind it doesn't.
The problem isn't the team. It's the distance between what a team can build now and what production demands.
That distance is the work.
BOOK A 30-MIN CALL →WHO WE WORK WITH
Teams at every stage.
Industries across the board.
No product yet
An idea that needs a team to build it
The vision is clear. The technical path isn't. You need a team that owns architecture, build, and launch without routing every decision back to you.
See how we build from scratch →Product needs fixing
A build that needs rescuing or rebuilding
A Replit prototype, a no-code ceiling, or a codebase a previous team walked away from. Real users, real demand, a foundation built for neither.
See how a rescue works →AI needs to work properly
AI features that have to hold in production
The model call works. Missing: the evals, the observability, and the architecture that keep it working when the data shifts and volume climbs.
See our AI engineering →Team needs capacity
Engineers who ramp fast and ship
Good engineers already in place, a backlog that keeps growing, senior time lost to review. Extra engineers who contribute in 15 days, not after a six-month search.
See the embedded team →Industries shipped in
HOW WE WORK
Inside your team. Not at arm's length.
Greenfield build, rescue, or augmentation. The working model stays the same.
In your repo.
On your Slack.
Yours when it ships.
Engineers work in your repository, follow your conventions, and answer in your Slack from day one. No client portal, no separate delivery squad, no weeks where the work disappears.
Every pull request is reviewable. Every architectural decision has a reason someone can explain. Nothing to hand over at the end, because ownership never moved.
Your cloud, your repo, your code. From Day 1.
The repository exists in your account before the first line is written. Infrastructure deploys to your cloud. When it closes: nothing to migrate, no access to revoke.
AI in the engineering loop, with gates that hold.
AI writes a significant share of what ships. A senior engineer reviews every line before merge. The velocity is real, and it doesn't survive without the gate.
HOW IT WORKS
From scope to production.
01
Scope & Align
WEEK 1
One week on scope before code starts. New build: stack, architecture, sprint plan confirmed. Rescue: a full audit of what survives and what gets replaced. Augmentation: a codebase walkthrough and a first sprint scoped. You leave with a document, not a verbal understanding.
02
Build & Show
WEEKS 2-5
Working software in Week 2. AI sits in the build loop from the first commit — the process, not a tool a few people have. Every pull request lands in your repo, where you can read it and push back.
03
Ship & Stay
WEEK 6+
Production on your cloud and your repo, with auth, error handling, logging, and test coverage. AI features also ship with evals and Langfuse tracing, so drift shows up on a dashboard, not in a complaint. Engineers stay reachable after go-live.
What's in your hands at each stage
WEEK 1
Architecture decision doc.
Scoped sprint plan, stack confirmed.
WEEK 2
First working software.
In your repo, not a Figma file.
WEEK 6
Production build.
Your cloud, your stack, your code.
AFTER
Engineers on Slack.
No handover cliff.
Visible from day one. Not delivered at the end.
BOOK A 30-MIN CALL →PROOF
Real products. Real numbers.
Verifiable on Clutch.
Availability checks converted to bookings
71%
AI Booking Agent · AI Systems
3 months, kickoff to live. Under 90 seconds from first message to confirmed booking.
Average AI response time
<2 sec
AI Marketing SaaS · Greenfield Build
4 months, idea to production. Ready for paying subscribers on launch day.
Crashes post-launch, 6 months
Zero
Venue booking SaaS
Crashing weekly before. Zero crashes in 6 months since.
TESTIMONIALS
5.0 on Clutch.
In their words.
★★★★★Verified across Clutch, Fiverr and Upwork
1 / 4
WHY SOCIILABS
Is the AI actually in the work, or just in the pitch?
Four questions worth asking any engineering partner in 2026.
Is AI in the build loop or in a slide?
AI writes a significant share of shipped code here. A senior engineer reviews every line before merge. The gate is what keeps velocity from turning into defect volume.
Does the AI in the product survive contact with users?
Shipping an LLM feature and keeping it reliable are separate problems. Evals catch regressions before release. Langfuse tracing makes a bad response investigable. Both go in during the build, not after the first incident.
Who owns it when the engagement ends?
Repository, cloud account, and IP sit with the client from the first commit. NDAs are standard. Nothing to transfer at the end, and no leverage held back.
What happens after launch?
Local Health Marketing has run on this arrangement for years. ZapBook is on an active multi-member retainer. Engagements that go well tend to continue rather than close.
5.0 on Clutch15+ years engineering10 projects shipped in 2025–2026
SECURITY & IP
Built so ownership never has to be negotiated.
Access control, IP assignment, and AI governance come standard on every engagement.
01
Your cloud. Your access. Your rules.
Work happens inside your infrastructure. We request the minimum access the project needs, revocable any day, and keep nothing after an engagement closes.
02
Your IP, signed before the first commit.
Everything built is assigned to you. NDAs are standard. No licensing surprises, no code held back, no lock-in written into the arrangement.
03
Every AI-written line reviewed before merge.
AI is in the coding loop. A senior engineer reviews its output before anything merges. Every change traces to a human owner.
04
Your codebase stays out of training data.
Client code is never used to train models. Provider selection follows your compliance requirements. Token spend and data residency get decided with you, not on your behalf.
Getting to a demo got cheap.Getting to production did not.
I've spent 15 years building software, and much of that time has gone into rescuing it. Insecure prototypes. Codebases left half-finished by previous teams. AI features that failed the week real users arrived.
The pattern is consistent. Products rarely fail at the demo. They fail at the foundation underneath it.
That foundation is what we build, and we stay accountable for it after launch.
Bring the problem. Leave with a plan.
HELP & SUPPORT
Questions about working with SociiLabs, answered straight.
AI sits in the build loop on every project, not as licenses a few engineers happen to have. It handles the mechanical work: scaffolding, test generation, refactors, migration scripts. A senior engineer reviews every line before merge, which is what keeps velocity from turning into defect volume. CodeRabbit found ~1.7× more major issues in AI-co-authored PRs without that review gate.