Services
What we do
AI-powered engineering excellence — from CodeForce teams delivering 3-6x productivity to innovative products and full-stack development services.
When software learns, the lifecycle has to learn with it.
SDLC was built for code with one job. Agentic systems reason, adapt, and rarely take the same path twice. We deliver them under a different lifecycle entirely — six phases, one feedback loop, every gate accountable to a human.
The shift, in one frame.
Agentic systems aren’t just faster software. They behave differently — non-deterministic, multi-surface, learning in production. The lifecycle has to match. Here’s where every assumption changes.
Six phases. One feedback loop.
Each phase maps onto an SDLC stage you’d recognize, but the work inside it changes. We’ve codified what we do at every gate so the engagement is legible from day one.
Discovery & Hypothesis
Before we touch architecture, we map the real workflow — where humans do repetitive judgment work, where the process breaks, and what the cost of failure actually is. Then we form testable hypotheses about where agents earn their keep.
- How it differs from SDLC
- Planning used to map features. Here, we map behaviors — because the same inputs can lead the system down different paths.
- How CodeForce delivers this
- A two-week discovery sprint led by a Principal Architect. Stakeholder interviews, workflow trace, and a written hypothesis register — each hypothesis tied to a measurable outcome.
- Artifacts you walk away with
- Workflow mapHypothesis registerGround-truth checklist
Where most teams stall.
We’ve watched four patterns repeat across teams shipping their first serious agentic system. Each one shows up only after production. ADLC is built to make these expensive failures cheap and early.
Most teams still go spec → build → ship. With agents, the cost of a wrong hypothesis multiplies in production. Simulation against real data is the cheapest place to be wrong; skipping it makes the next six months a recovery.
Prompts are now part of your logic surface — equal weight to code. Without version control, review, and eval, you’re shipping an unobservable codebase that changes silently every time a prompt is tweaked.
Functional tests don’t catch what actually breaks agents — hallucination drift, reasoning regressions, cost spikes from a model swap. You need a different test paradigm, instrumented before deployment, not after.
Models drift. Context windows fill with noise. Tool dependencies change underneath you. Without behavioral monitoring and explicit drift alerts, you find out from your users — and by then the trust loss is already booked.
Three doors into the lifecycle.
Pick where you want us. Full Team covers all six phases as an embedded extension of your team. On-Demand plugs into specific phases when you need surge. Managed means we own the outcome end-to-end.
What we deploy underneath.
The stack is opinionated on purpose. Every layer is here because something fails without it — and most teams discover that after shipping. We bring the answers in.
A single orchestration layer manages the agent fleet. You manage the manager, not five concurrent sessions.
Eval criteria are written in phase 03, before code. Every change is gated against them before merge.
Production tells you what eval missed. Signals route back to the next discovery cycle — that’s the loop.
Client code never trains external models. Every reasoning gate is logged. Every deploy is reversible.
For the CTO doing diligence.
Real questions we hear in week one of evaluation. Plain answers — no marketing softening.
Who owns the IP — including prompts and trained context?
How do you handle hallucinations and rollback?
What does evaluation actually look like in production?
How is this priced — by sprint, by outcome, or by team?
Where do humans actually gate the agents?
How do you keep our code out of training data?
Start where the cost of being wrong is lowest.
A four-week Proof of Value pilot. Two senior engineers plus an agent fleet against a real workflow you pick. 160–200 story points. Three success criteria signed off before kickoff: 3× velocity against your baseline, zero production defects from agent-written code, and a clean handoff if you stop there.
Most pilots start within two weeks of signed scoping. No long-term contract required.
Engineering Services
End-to-end product development and maintenance — from initial design through production deployment and beyond. We partner with enterprises, ISVs, and startups to build world-class software products.
Our engineering teams bring deep expertise across the full technology stack, combining traditional software engineering excellence with cutting-edge AI capabilities.
- Product Development — From concept to launch, we build products that scale
- Design & UX — User-centered design that drives adoption and engagement
- Production Operations — DevOps, monitoring, and continuous improvement
- Maintenance & Support — Long-term partnership for ongoing success
Our Engineering Process
Products
Our industry-agnostic Agentic AI solutions transform how enterprises operate and compete. Built on cutting-edge multi-agent architectures, our products deliver intelligent automation that adapts and evolves with your business.
Each product is designed to integrate seamlessly into your existing workflows, providing immediate value while laying the foundation for deeper AI transformation.
Our Agentic AI products are coming soon. Contact us to learn more about early access opportunities.
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